Benchmarking short sequence mapping tools

Human-specific epigenomic states in spermatogenesis

Infertility is becoming increasingly common, affecting one in six people globally. Half of these cases can be attributed to male factors, many driven by abnormalities in the process of sperm development. Emerging evidence from genome-wide association studies, genetic screening of patient cohorts, and animal models highlights an important genetic contribution to spermatogenic defects, but comprehensive identification and characterization of the genes critical for male fertility remain lacking. High divergence of gene regulation in spermatogenic cells across species poses challenges for delineating the genetic pathways required for human spermatogenesis using common model organisms. In this study, we leveraged post-translational histone modification and gene transcription data for 15,491 genes in four mammalian species (human, rhesus macaque, mouse, and opossum), to identify human-specific patterns of gene regulation during spermatogenesis. We combined H3K27me3 ChIP-seq, H3K4me3 ChIP-seq, and RNA-seq data to define epigenetic states for each gene at two stages of spermatogenesis, pachytene spermatocytes and round spermatids, in each species. We identified 239 genes that are uniquely active, poised, or dynamically regulated in human spermatogenic cells distinct from the other three species. While some of these genes have been implicated in reproductive functions, many more have not yet been associated with human infertility and may be candidates for further molecular and epidemiologic studies. Our analysis offers an example of the opportunities provided by evolutionary and epigenomic data for broadly screening candidate genes implicated in reproduction, which might lead to discoveries of novel genetic targets for diagnosis and management of male infertility and male contraception.

SigAlign: an alignment algorithm guided by explicit similarity criteria

In biological sequence alignment, prevailing heuristic aligners achieve high-throughput by several approximation techniques, but at the cost of sacrificing the clarity of output criteria and creating complex parameter spaces. To surmount these challenges, we introduce 'SigAlign', a novel alignment algorithm that employs two explicit cutoffs for the results: minimum length and maximum penalty per length, alongside three affine gap penalties. Comparative analyses of SigAlign against leading database search tools (BLASTn, MMseqs2) and read mappers (BWA-MEM, bowtie2, HISAT2, minimap2) highlight its performance in read mapping and database searches. Our research demonstrates that SigAlign not only provides high sensitivity with a non-heuristic approach, but also surpasses the throughput of existing heuristic aligners, particularly for high-accuracy reads or genomes with few repetitive regions. As an open-source library, SigAlign is poised to become a foundational component to provide a transparent and customizable alignment process to new analytical algorithms, tools and pipelines in bioinformatics.

Benchmarking of five NGS mapping tools for the reference alignment of bacterial outer membrane vesicles-associated small RNAs

Advances in small RNAs (sRNAs)-related studies have posed a challenge for NGS-related bioinformatics, especially regarding the correct mapping of sRNAs. Depending on the algorithms and scoring matrices on which they are based, aligners are influenced by the characteristics of the dataset and the reference genome. These influences have been studied mainly in eukaryotes and to some extent in prokaryotes. However, in bacteria, the selection of aligners depending on sRNA-seq data associated with outer membrane vesicles (OMVs) and the features of the corresponding bacterial reference genome has not yet been investigated. We selected five aligners: BBmap, Bowtie2, BWA, Minimap2 and Segemehl, known for their generally good performance, to test them in mapping OMV-associated sRNAs from Aliivibrio fischeri to the bacterial reference genome. Significant differences in the performance of the five aligners were observed, resulting in differential recognition of OMV-associated sRNA biotypes in A. fischeri. Our results suggest that aligner(s) should not be arbitrarily selected for this task, which is often done, as this can be detrimental to the biological interpretation of NGS analysis results. Since each aligner has specific advantages and disadvantages, these need to be considered depending on the characteristics of the input OMV sRNAs dataset and the corresponding bacterial reference genome to improve the detection of existing, biologically important OMV sRNAs. Until we learn more about these dependencies, we recommend using at least two, preferably three, aligners that have good metrics for the given dataset/bacterial reference genome. The overlapping results should be considered trustworthy, yet their differences should not be dismissed lightly, but treated carefully in order not to overlook any biologically important OMV sRNA. This can be achieved by applying the intersect-then-combine approach. For the mapping of OMV-associated sRNAs of A. fischeri to the reference genome organized into two circular chromosomes and one circular plasmid, containing copies of sequences with rRNA- and tRNA-related features and no copies of sequences with protein-encoding features, if the aligners are used with their default parameters, we advise avoiding Segemehl, and recommend using the intersect-then-combine approach with BBmap, BWA and Minimap2 to improve the potential for discovery of biologically important OMV-associated sRNAs.

Wastewater surveillance together with metaviromic data revealed the unusual resurgence of infectious diseases after the first wave of the COVID-19 outbreak

How to address public health priorities after COVID-19 is becoming a critical task. To this end, we conducted wastewater surveillance for six leading pathogens, namely, SARS-CoV-2, norovirus, rotavirus, influenza A virus (IAV), enteroviruses and respiratory syncytial virus (RSV), in Nanchang city from January to April 2023. Metaviromic sequencing was conducted at the 1st, 4th, 7th, 9th, 12th and 14th weeks to reveal the dynamics of viral pathogens that were not covered by qPCR. Amplicon sequencing of the conserved region of norovirus GI and GII and the rotavirus and region encoding nonstructural protein of RSV was also conducted weekly. The results showed that after a rapid decrease in SARS-CoV-2 sewage concentrations occurred in January 2023, surges of norovirus, rotavirus, IAV and RSV started at the 6th, 7th, 8th and 11th weeks, respectively. The dynamics of the sewage concentrations of norovirus, rotavirus, IAV and RSV were consistent with the off-season resurgence of the above infectious diseases. Notably, peak sewage concentrations of norovirus GI, GII, rotavirus, IAV and RSV were found at the 6th, 3rd, 7th, 7th and 8th weeks, respectively. Astroviruses also resurge after the 7th week, as revealed by metaviromic data, suggesting that wastewater surveillance together with metaviromic data provides an essential early warning tool for revealing patterns of infectious disease resurgence.

Unravelling reference bias in ancient DNA datasets

MOTIVATION

The alignment of sequencing reads is a critical step in the characterization of ancient genomes. However, reference bias and spurious mappings pose a significant challenge, particularly as cutting-edge wet lab methods generate datasets that push the boundaries of alignment tools. Reference bias occurs when reference alleles are favoured over alternative alleles during mapping, whereas spurious mappings stem from either contamination or when endogenous reads fail to align to their correct position. Previous work has shown that these phenomena are correlated with read length but a more thorough investigation of reference bias and spurious mappings for ancient DNA has been lacking. Here, we use a range of empirical and simulated palaeogenomic datasets to investigate the impacts of mapping tools, quality thresholds, and reference genome on mismatch rates across read lengths.

RESULTS

For these analyses, we introduce AMBER, a new bioinformatics tool for assessing the quality of ancient DNA mapping directly from BAM-files and informing on reference bias, read length cut-offs and reference selection. AMBER rapidly and simultaneously computes the sequence read mapping bias in the form of the mismatch rates per read length, cytosine deamination profiles at both CpG and non-CpG sites, fragment length distributions, and genomic breadth and depth of coverage. Using AMBER, we find that mapping algorithms and quality threshold choices dictate reference bias and rates of spurious alignment at different read lengths in a predictable manner, suggesting that optimized mapping parameters for each read length will be a key step in alleviating reference bias and spurious mappings.

AVAILABILITY AND IMPLEMENTATION

AMBER is available for noncommercial use on GitHub (https://github.com/tvandervalk/AMBER.git). Scripts used to generate and analyse simulated datasets are available on Github (https://github.com/sdolenz/refbias_scripts).

Platinum Nanoparticles Prevent the Resistance of Pseudomonas aeruginosa to Ciprofloxacin and Imipenem: Mechanism Insights

Metal nanoparticles (MNPs) have recently gained extensive attention due to their broad-spectrum prospect, particularly in biomedical application. Here, we reveal that long-term exposure to platinum nanoparticles (Pt NPs) increases the susceptibility of Pseudomonas aeruginosa PAO1 to imipenem and ciprofloxacin. We exposed PAO1 to Pt NPs (a series of doses, varying from 0.125 to 35 μg/mL) for 60 days and characterized the evolved strains (ES) and compared with wild type (WT) to understand the mechanism of heightened sensitivity. We found that overexpression of oprD and downregulation of mexEF-oprN facilitate the intracellular accumulation of antibiotic, thus increasing susceptibility. Furthermore, loss-of-function mutations were discovered in regulators lasR and mexT. Cloning intact lasR from wild-type (WT) into ES slightly improves imipenem resistance. Strikingly, cloning mexT from WT into ES reverts the imipenem and ciprofloxacin resistance to the original level. Briefly, the increase of membrane permeability controlled by mexT made PAO1 greatly susceptible to imipenem and ciprofloxacin, and the decrease of quorum sensing mediated by lasR made PAO1 slightly susceptible to imipenem. Overall, these results reveal an antibiotic susceptibility mechanism from prolonged exposure to MNPs, which provides a promising approach to prevent antibiotic resistance.

Evaluation of an optimized germline exomes pipeline using BWA-MEM2 and Dragen-GATK tools

The next-generation sequencing (NGS) technology represents a significant advance in genomics and medical diagnosis. Nevertheless, the time it takes to perform sequencing, data analysis, and variant interpretation is a bottleneck in using next-generation sequencing in precision medicine. For accurate and efficient performance in clinical diagnostic lab practice, a consistent data analysis pipeline is necessary to avoid false variant calls and achieve optimum accuracy. This study aims to compare the performance of two NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM and BWA-MEM2) and variant calling (GATK-HaplotypeCaller and DRAGEN-GATK). On Whole Exome Sequencing (WES) data, computational performance was assessed using several criteria, including mapping efficiency, variant calling performance, false positive calls rate, and time. We examined four gold-standard WES data sets: Ashkenazim father (NA24149), Ashkenazim mother (NA24143), Ashkenazim son (NA24385), and Asian son (NA25631). In addition, eighteen exome samples were analyzed based on different read counts, and coverage was used precisely in the run-time assessment. By using BWA-MEM 2 and Dragen-GATK, this study achieved faster and more accurate detection for SNVs and indels than the standard GATK Best Practices workflow. This systematic comparison will enable the bioinformatics community to develop a more efficient and faster solution for analyzing NGS data.

Target capture and genome skimming for plant diversity studies

Recent technological advances in long-read high-throughput sequencing and assembly methods have facilitated the generation of annotated chromosome-scale whole-genome sequence data for evolutionary studies; however, generating such data can still be difficult for many plant species. For example, obtaining high-molecular-weight DNA is typically impossible for samples in historical herbarium collections, which often have degraded DNA. The need to fast-freeze newly collected living samples to conserve high-quality DNA can be complicated when plants are only found in remote areas. Therefore, short-read reduced-genome representations, such as target capture and genome skimming, remain important for evolutionary studies. Here, we review the pros and cons of each technique for non-model plant taxa. We provide guidance related to logistics, budget, the genomic resources previously available for the target clade, and the nature of the study. Furthermore, we assess the available bioinformatic analyses, detailing best practices and pitfalls, and suggest pathways to combine newly generated data with legacy data. Finally, we explore the possible downstream analyses allowed by the type of data generated using each technique. We provide a practical guide to help researchers make the best-informed choice regarding reduced genome representation for evolutionary studies of non-model plants in cases where whole-genome sequencing remains impractical.

Somatic CNV Detection by Single-Cell Whole-Genome Sequencing in Postmortem Human Brain

The evidence for a role of somatic mutations, including copy-number variants (CNVs), in neurodegeneration has increased in the last decade. However, the understanding of the types and origins of these mutations, and their exact contributions to disease onset and progression, is still in its infancy. The use of single-cell (or nuclear) whole-genome sequencing (scWGS) has emerged as a powerful tool to answer these questions. In the present chapter, we provide laboratory and bioinformatic protocols used successfully in our lab to detect megabase-scale CNVs in single cells from multiple system atrophy (MSA) human postmortem brains, using immunolabeling prior to selection of nuclei for whole-genome amplification (WGA). We also present an unpublished comparison of scWGS generated from the same control substantia nigra (SN) sample, using the latest versions of popular WGA chemistries, MDA and PicoPLEX. We have used this protocol to focus on brain cell types most relevant to synucleinopathies (dopaminergic [DA] neurons in Parkinson's disease [PD] and oligodendrocytes in MSA), but it can be applied to any tissue and/or cell type with appropriate markers.

Strategy for RNA-Seq Experimental Design and Data Analysis

Ribonucleic acids (RNAs) are fundamental molecules that control regulation and expression of the genome and therefore the function of a cell. Robust analysis and quantification of RNA transcripts hold critical importance in understanding cell function, altered phenotypes in different biological context, for understanding and targeting diseases. The development of RNA-sequencing (RNA-Seq) now provides opportunities to analyze the expression and function of RNA molecules at an unprecedented scale. However, the strategy for RNA-Seq experimental design and data analysis can substantially differ depending on the biological application. The design choice could also have significant impact for downstream results and interpretation of data. Here we describe key critical considerations required for RNA-Seq experimental design and also describe a step-by-step bioinformatics workflow detailing the different steps required for RNA-Seq data analysis. We believe this article will be a valuable guide for designing and analyzing RNA-Seq data to address a wide range of different biological questions.

Rapid genome sequencing for pediatrics

The advancements made in next-generation sequencing (NGS) technology over the past two decades have transformed our understanding of genetic variation in humans and had a profound impact on our ability to diagnose patients with rare genetic diseases. In this review, we discuss the recently developed application of rapid NGS techniques, used to diagnose pediatric patients with suspected rare diseases who are critically ill. We highlight the challenges associated with performing such clinical diagnostics tests in terms of the laboratory infrastructure, bioinformatic analysis pipelines, and the ethical considerations that need to be addressed. We end by looking at what future developments in this field may look like and how they can be used to augment the genetic data to further improve the diagnostic rates for these high-priority patients.

Phylogenomics reveals extensive introgression and a case of mito-nuclear discordance in the killifish genus Kryptolebias

Introgression is a widespread evolutionary process leading to phylogenetic inconsistencies among distinct parts of the genomes, particularly between mitochondrial and nuclear-based phylogenetic reconstructions (e.g., mito-nuclear discordances). Here, we used mtDNA and genome-wide nuclear sites to provide the first phylogenomic-based hypothesis on the evolutionary relationships within the killifish genus Kryptolebias. In addition, we tested for evidence of past introgression in the genus given the multiple reports of undergoing hybridization between its members. Our mtDNA phylogeny generally agreed with the relationships previously proposed for the genus. However, our reconstruction based on nuclear DNA revealed an unknown lineage - Kryptolebias sp. 'ESP' - as the sister group of the self-fertilizing mangrove killifishes, K. marmoratus and K. hermaphroditus. All individuals sequenced of Kryptolebias sp. 'ESP' had the same mtDNA haplotype commonly observed in K. hermaphroditus, demonstrating a clear case of mito-nuclear discordance. Our analysis further confirmed extensive history of introgression between Kryptolebias sp. 'ESP' and K. hermaphroditus. Population genomics analyses indicate no current gene flow between the two lineages, despite their current sympatry and history of introgression. We also confirmed introgression between other species pairs in the genus that have been recently reported to form hybrid zones. Overall, our study provides a phylogenomic reconstruction covering most of the Kryptolebias species, reveals a new lineage hidden in a case of mito-nuclear discordance, and provides evidence of multiple events of ancestral introgression in the genus. These findings underscore the importance of investigating different genomic information in a phylogenetic framework, particularly in taxa where introgression is common as in the sexually diverse mangrove killifishes.

Comparative Transcriptomic Analysis of Root and Leaf Transcript Profiles Reveals the Coordinated Mechanisms in Response to Salinity Stress in Common Vetch

Owing to its strong environmental suitability to adverse abiotic stress conditions, common vetch (Vicia sativa) is grown worldwide for both forage and green manure purposes and is an important protein source for human consumption and livestock feed. The germination of common vetch seeds and growth of seedlings are severely affected by salinity stress, and the response of common vetch to salinity stress at the molecular level is still poorly understood. In this study, we report the first comparative transcriptomic analysis of the leaves and roots of common vetch under salinity stress. A total of 6361 differentially expressed genes were identified in leaves and roots. In the roots, the stress response was dominated by genes involved in peroxidase activity. However, the genes in leaves focused mainly on Ca²⁺ transport. Overexpression of six salinity-inducible transcription factors in yeast further confirmed their biological functions in the salinity stress response. Our study provides the most comprehensive transcriptomic analysis of common vetch leaf and root responses to salinity stress. Our findings broaden the knowledge of the common and distinct intrinsic molecular mechanisms within the leaves and roots of common vetch and could help to develop common vetch cultivars with high salinity tolerance.

cpn60 barcode sequences accurately identify newly defined genera within the Lactobacillaceae

The cpn60 barcode sequence is established as an informative target for microbial species identification. Applications of cpn60 barcode sequencing are supported by the availability of "universal" PCR primers for its amplification and a curated reference database of cpn60 sequences, cpnDB. A recent reclassification of lactobacilli involving the definition of 23 new genera provided an opportunity to update cpnDB and to determine if the cpn60 barcode could be used for accurate identification of species consistent with the new framework. Analysis of 275 cpn60 sequences representing 258/269 of the validly named species in Lactobacillus, Paralactobacillus and the 23 newer genera showed that cpn60-based sequence relationships were generally consistent with the whole-genome-based phylogeny. Aligning or mapping full length barcode sequences or a 150 bp subsequence resulted in accurate and unambiguous species identification in almost all cases. Taken together, our results show that the combination of available reference sequence data, "universal" barcode amplification primers, and the inherent sequence diversity within the cpn60 barcode make it a useful target for the detection and identification of lactobacilli as defined by the latest taxonomic framework.

Sustained software development, not number of citations or journal choice, is indicative of accurate bioinformatic software

Background

Computational biology provides software tools for testing and making inferences about biological data. In the face of increasing volumes of data, heuristic methods that trade software speed for accuracy may be employed. We have studied these trade-offs using the results of a large number of independent software benchmarks, and evaluated whether external factors, including speed, author reputation, journal impact, recency and developer efforts, are indicative of accurate software.

Results

We find that software speed, author reputation, journal impact, number of citations and age are unreliable predictors of software accuracy. This is unfortunate because these are frequently cited reasons for selecting software tools. However, GitHub-derived statistics and high version numbers show that accurate bioinformatic software tools are generally the product of many improvements over time. We also find an excess of slow and inaccurate bioinformatic software tools, and this is consistent across many sub-disciplines. There are few tools that are middle-of-road in terms of accuracy and speed trade-offs.

Conclusions

Our findings indicate that accurate bioinformatic software is primarily the product of long-term commitments to software development. In addition, we hypothesise that bioinformatics software suffers from publication bias. Software that is intermediate in terms of both speed and accuracy may be difficult to publish—possibly due to author, editor and reviewer practises. This leaves an unfortunate hole in the literature, as ideal tools may fall into this gap. High accuracy tools are not always useful if they are slow, while high speed is not useful if the results are also inaccurate.

Supplementary Information

The online version contains supplementary material available at (10.1186/s13059-022-02625-x).

Assessing reproducibility of inherited variants detected with short-read whole genome sequencing

BACKGROUND

Reproducible detection of inherited variants with whole genome sequencing (WGS) is vital for the implementation of precision medicine and is a complicated process in which each step affects variant call quality. Systematically assessing reproducibility of inherited variants with WGS and impact of each step in the process is needed for understanding and improving quality of inherited variants from WGS.

RESULTS

To dissect the impact of factors involved in detection of inherited variants with WGS, we sequence triplicates of eight DNA samples representing two populations on three short-read sequencing platforms using three library kits in six labs and call variants with 56 combinations of aligners and callers. We find that bioinformatics pipelines (callers and aligners) have a larger impact on variant reproducibility than WGS platform or library preparation. Single-nucleotide variants (SNVs), particularly outside difficult-to-map regions, are more reproducible than small insertions and deletions (indels), which are least reproducible when > 5 bp. Increasing sequencing coverage improves indel reproducibility but has limited impact on SNVs above 30×.

CONCLUSIONS

Our findings highlight sources of variability in variant detection and the need for improvement of bioinformatics pipelines in the era of precision medicine with WGS.

Technology dictates algorithms: recent developments in read alignment

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

Dealing with Pseudogenes in Molecular Diagnostics in the Next Generation Sequencing Era

Presence of pseudogenes is a dreadful issue in next generation sequencing (NGS), because their contamination can interfere with the detection of variants in the genuine gene and generate false positive and false negative variants.In this chapter we focus on issues related to the application of NGS strategies for analysis of genes with pseudogenes in a clinical setting. The degree to which a pseudogene impacts the ability to accurately detect and map variants in its parent gene depends on the degree of similarity (homology) with the parent gene itself. Hereby, target enrichment and mapping strategies are crucial factors to avoid "contaminating" pseudogene sequences. For target enrichment, we describe advantages and disadvantages of PCR- and capture-based strategies. For mapping strategies, we discuss crucial parameters that need to be considered to accurately distinguish sequences of functional genes from pseudogenic sequences. Finally, we discuss some examples of genes associated with Mendelian disorders, for which interesting NGS approaches are described to avoid interference with pseudogene sequences.

New evaluation methods of read mapping by 17 aligners on simulated and empirical NGS data: an updated comparison of DNA- and RNA-Seq data from Illumina and Ion Torrent technologies

During the last (15) years, improved omics sequencing technologies have expanded the scale and resolution of various biological applications, generating high-throughput datasets that require carefully chosen software tools to be processed. Therefore, following the sequencing development, bioinformatics researchers have been challenged to implement alignment algorithms for next-generation sequencing reads. However, nowadays selection of aligners based on genome characteristics is poorly studied, so our benchmarking study extended the “state of art” comparing 17 different aligners. The chosen tools were assessed on empirical human DNA- and RNA-Seq data, as well as on simulated datasets in human and mouse, evaluating a set of parameters previously not considered in such kind of benchmarks. As expected, we found that each tool was the best in specific conditions. For Ion Torrent single-end RNA-Seq samples, the most suitable aligners were CLC and BWA-MEM, which reached the best results in terms of efficiency, accuracy, duplication rate, saturation profile and running time. About Illumina paired-end osteomyelitis transcriptomics data, instead, the best performer algorithm, together with the already cited CLC, resulted Novoalign, which excelled in accuracy and saturation analyses. Segemehl and DNASTAR performed the best on both DNA-Seq data, with Segemehl particularly suitable for exome data. In conclusion, our study could guide users in the selection of a suitable aligner based on genome and transcriptome characteristics. However, several other aspects, emerged from our work, should be considered in the evolution of alignment research area, such as the involvement of artificial intelligence to support cloud computing and mapping to multiple genomes.

Estimating sequencing error rates using families

BACKGROUND

As next-generation sequencing technologies make their way into the clinic, knowledge of their error rates is essential if they are to be used to guide patient care. However, sequencing platforms and variant-calling pipelines are continuously evolving, making it difficult to accurately quantify error rates for the particular combination of assay and software parameters used on each sample. Family data provide a unique opportunity for estimating sequencing error rates since it allows us to observe a fraction of sequencing errors as Mendelian errors in the family, which we can then use to produce genome-wide error estimates for each sample.

RESULTS

We introduce a method that uses Mendelian errors in sequencing data to make highly granular per-sample estimates of precision and recall for any set of variant calls, regardless of sequencing platform or calling methodology. We validate the accuracy of our estimates using monozygotic twins, and we use a set of monozygotic quadruplets to show that our predictions closely match the consensus method. We demonstrate our method's versatility by estimating sequencing error rates for whole genome sequencing, whole exome sequencing, and microarray datasets, and we highlight its sensitivity by quantifying performance increases between different versions of the GATK variant-calling pipeline. We then use our method to demonstrate that: 1) Sequencing error rates between samples in the same dataset can vary by over an order of magnitude. 2) Variant calling performance decreases substantially in low-complexity regions of the genome. 3) Variant calling performance in whole exome sequencing data decreases with distance from the nearest target region. 4) Variant calls from lymphoblastoid cell lines can be as accurate as those from whole blood. 5) Whole-genome sequencing can attain microarray-level precision and recall at disease-associated SNV sites.

CONCLUSION

Genotype datasets from families are powerful resources that can be used to make fine-grained estimates of sequencing error for any sequencing platform and variant-calling methodology.

Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels

The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.

Vaccine targeting SIVmac251 protease cleavage sites protects macaques against vaginal infection

After over 3 decades of research, an effective anti-HIV vaccine remains elusive. The recently halted HVTN702 clinical trial not only further stresses the challenge to develop an effective HIV vaccine but also emphasizes that unconventional and novel vaccine strategies are urgently needed. Here, we report that a vaccine focusing the immune response on the sequences surrounding the 12 viral protease cleavage sites (PCSs) provided greater than 80% protection to Mauritian cynomolgus macaques against repeated intravaginal SIVmac251 challenges. The PCS-specific T cell responses correlated with vaccine efficacy. The PCS vaccine did not induce immune activation or inflammation known to be associated with increased susceptibility to HIV infection. Machine learning analyses revealed that the immune microenvironment generated by the PCS vaccine was predictive of vaccine efficacy. Our study demonstrates, for the first time to our knowledge, that a vaccine which targets only viral maturation, but lacks full-length Env and Gag immunogens, can prevent intravaginal infection in a stringent macaque/SIV challenge model. Targeting HIV maturation thus offers a potentially novel approach to developing an effective HIV vaccine.

High-Throughput Sequencing to Detect DNA-RNA Changes

The advent of deep sequencing technologies has greatly improved the study of complex eukaryotic genomes and transcriptomes, allowing the investigation of posttranscriptional molecular mechanisms as alternative splicing and RNA editing at unprecedented throughput and resolution. The most prevalent type of RNA editing in higher eukaryotes is the deamination of adenosine to inosine (A-to-I) in double-stranded RNAs. Depending on the RNA type or the RNA region involved, A-to-I RNA editing contributes to the transcriptome and proteome diversity.Hereafter, we present an easy and reproducible computational protocol for the identification of candidate RNA editing sites in humans using deep transcriptome (RNA-Seq) and genome (DNA-Seq) sequencing.

Next Generation Sequencing and Bioinformatics Analysis of Family Genetic Inheritance

Mendelian and complex genetic trait diseases continue to burden and affect society both socially and economically. The lack of effective tests has hampered diagnosis thus, the affected lack proper prognosis. Mendelian diseases are caused by genetic mutations in a singular gene while complex trait diseases are caused by the accumulation of mutations in either linked or unlinked genomic regions. Significant advances have been made in identifying novel diseases associated mutations especially with the introduction of next generation and third generation sequencing. Regardless, some diseases are still without diagnosis as most tests rely on SNP genotyping panels developed from population based genetic analyses. Analysis of family genetic inheritance using whole genomes, whole exomes or a panel of genes has been shown to be effective in identifying disease-causing mutations. In this review, we discuss next generation and third generation sequencing platforms, bioinformatic tools and genetic resources commonly used to analyze family based genomic data with a focus on identifying inherited or novel disease-causing mutations. Additionally, we also highlight the analytical, ethical and regulatory challenges associated with analyzing personal genomes which constitute the data used for family genetic inheritance.

Impact of RNA-seq data analysis algorithms on gene expression estimation and downstream prediction

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline's performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

CONTEXT.—

Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical laboratory setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories.

OBJECTIVE.—

To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipeline as a part of a new clinical NGS assay.

DATA SOURCES.—

This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation.

CONCLUSIONS.—

This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

Streamlining Quantitative Analysis of Long RNA Sequencing Reads

Transcriptome analyses allow for linking RNA expression profiles to cellular pathways and phenotypes. Despite improvements in sequencing methodology, whole transcriptome analyses are still tedious, especially for methodologies producing long reads. Currently, available data analysis software often lacks cost- and time-efficient workflows. Although kit-based workflows and benchtop platforms for RNA sequencing provide software options, e.g., cloud-based tools to analyze basecalled reads, quantitative, and easy-to-use solutions for transcriptome analysis, especially for non-human data, are missing. We therefore developed a user-friendly tool, termed Alignator, for rapid analysis of long RNA reads requiring only FASTQ files and an Ensembl cDNA database reference. After successful mapping, Alignator generates quantitative information for each transcript and provides a table in which sequenced and aligned RNA are stored for further comparative analyses.

T-lex3: an accurate tool to genotype and estimate population frequencies of transposable elements using the latest short-read whole genome sequencing data

Motivation

Transposable elements (TEs) constitute a significant proportion of the majority of genomes sequenced to date. TEs are responsible for a considerable fraction of the genetic variation within and among species. Accurate genotyping of TEs in genomes is therefore crucial for a complete identification of the genetic differences among individuals, populations and species.

Results

In this work, we present a new version of T-lex, a computational pipeline that accurately genotypes and estimates the population frequencies of reference TE insertions using short-read high-throughput sequencing data. In this new version, we have re-designed the T-lex algorithm to integrate the BWA-MEM short-read aligner, which is one of the most accurate short-read mappers and can be launched on longer short-reads (e.g. reads >150 bp). We have added new filtering steps to increase the accuracy of the genotyping, and new parameters that allow the user to control both the minimum and maximum number of reads, and the minimum number of strains to genotype a TE insertion. We also showed for the first time that T-lex3 provides accurate TE calls in a plant genome.

Availability and implementation

To test the accuracy of T-lex3, we called 1630 individual TE insertions in Drosophila melanogaster, 1600 individual TE insertions in humans, and 3067 individual TE insertions in the rice genome. We showed that this new version of T-lex is a broadly applicable and accurate tool for genotyping and estimating TE frequencies in organisms with different genome sizes and different TE contents. T-lex3 is available at Github: https://github.com/GonzalezLab/T-lex3.

Supplementary information

Supplementary data are available at Bioinformatics online.

Shouji: a fast and efficient pre-alignment filter for sequence alignment

MOTIVATION

The ability to generate massive amounts of sequencing data continues to overwhelm the processing capability of existing algorithms and compute infrastructures. In this work, we explore the use of hardware/software co-design and hardware acceleration to significantly reduce the execution time of short sequence alignment, a crucial step in analyzing sequenced genomes. We introduce Shouji, a highly parallel and accurate pre-alignment filter that remarkably reduces the need for computationally-costly dynamic programming algorithms. The first key idea of our proposed pre-alignment filter is to provide high filtering accuracy by correctly detecting all common subsequences shared between two given sequences. The second key idea is to design a hardware accelerator that adopts modern field-programmable gate array (FPGA) architectures to further boost the performance of our algorithm.

RESULTS

Shouji significantly improves the accuracy of pre-alignment filtering by up to two orders of magnitude compared to the state-of-the-art pre-alignment filters, GateKeeper and SHD. Our FPGA-based accelerator is up to three orders of magnitude faster than the equivalent CPU implementation of Shouji. Using a single FPGA chip, we benchmark the benefits of integrating Shouji with five state-of-the-art sequence aligners, designed for different computing platforms. The addition of Shouji as a pre-alignment step reduces the execution time of the five state-of-the-art sequence aligners by up to 18.8×. Shouji can be adapted for any bioinformatics pipeline that performs sequence alignment for verification. Unlike most existing methods that aim to accelerate sequence alignment, Shouji does not sacrifice any of the aligner capabilities, as it does not modify or replace the alignment step.

AVAILABILITY AND IMPLEMENTATION

https://github.com/CMU-SAFARI/Shouji.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA methylation analysis in plants: review of computational tools and future perspectives

Genome-wide DNA methylation studies have quickly expanded due to advances in next-generation sequencing techniques along with a wealth of computational tools to analyze the data. Most of our knowledge about DNA methylation profiles, epigenetic heritability and the function of DNA methylation in plants derives from the model species Arabidopsis thaliana. There are increasingly many studies on DNA methylation in plants-uncovering methylation profiles and explaining variations in different plant tissues. Additionally, DNA methylation comparisons of different plant tissue types and dynamics during development processes are only slowly emerging but are crucial for understanding developmental and regulatory decisions. Translating this knowledge from plant model species to commercial crops could allow the establishment of new varieties with increased stress resilience and improved yield. In this review, we provide an overview of the most commonly applied bioinformatics tools for the analysis of DNA methylation data (particularly bisulfite sequencing data). The performances of a selection of the tools are analyzed for computational time and agreement in predicted methylated sites for A. thaliana, which has a smaller genome compared to the hexaploid bread wheat. The performance of the tools was benchmarked on five plant genomes. We give examples of applications of DNA methylation data analysis in crops (with a focus on cereals) and an outlook for future developments for DNA methylation status manipulations and data integration.

scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data

Single-cell chromatin accessibility sequencing has become a powerful technology for understanding epigenetic heterogeneity of complex tissues. However, there is a lack of open-source software for comprehensive processing, analysis, and visualization of such data generated using all existing experimental protocols. Here, we present scATAC-pro for quality assessment, analysis, and visualization of single-cell chromatin accessibility sequencing data. scATAC-pro computes a range of quality control metrics for several key steps of experimental protocols, with a flexible choice of methods. It generates summary reports for both quality assessment and downstream analysis. scATAC-pro is available at https://github.com/tanlabcode/scATAC-pro.

SRPRISM (Single Read Paired Read Indel Substitution Minimizer): an efficient aligner for assemblies with explicit guarantees

BACKGROUND

Alignment of sequence reads generated by next-generation sequencing is an integral part of most pipelines analyzing next-generation sequencing data. A number of tools designed to quickly align a large volume of sequences are already available. However, most existing tools lack explicit guarantees about their output. They also do not support searching genome assemblies, such as the human genome assembly GRCh38, that include primary and alternate sequences and placement information for alternate sequences to primary sequences in the assembly.

FINDINGS

This paper describes SRPRISM (Single Read Paired Read Indel Substitution Minimizer), an alignment tool for aligning reads without splices. SRPRISM has features not available in most tools, such as (i) support for searching genome assemblies with alternate sequences, (ii) partial alignment of reads with a specified region of reads to be included in the alignment, (iii) choice of ranking schemes for alignments, and (iv) explicit criteria for search sensitivity. We compare the performance of SRPRISM to GEM, Kart, STAR, BWA-MEM, Bowtie2, Hobbes, and Yara using benchmark sets for paired and single reads of lengths 100 and 250 bp generated using DWGSIM. SRPRISM found the best results for most benchmark sets with error rate of up to ∼2.5% and GEM performed best for higher error rates. SRPRISM was also more sensitive than other tools even when sensitivity was reduced to improve run time performance.

CONCLUSIONS

We present SRPRISM as a flexible read mapping tool that provides explicit guarantees on results.

Differential Expression Analysis of RNA-seq Reads: Overview, Taxonomy, and Tools

Analysis of RNA-sequence (RNA-seq) data is widely used in transcriptomic studies and it has many applications. We review RNA-seq data analysis from RNA-seq reads to the results of differential expression analysis. In addition, we perform a descriptive comparison of tools used in each step of RNA-seq data analysis along with a discussion of important characteristics of these tools. A taxonomy of tools is also provided. A discussion of issues in quality control and visualization of RNA-seq data is also included along with useful tools. Finally, we provide some guidelines for the RNA-seq data analyst, along with research issues and challenges which should be addressed.

Toward a Dynamic Threshold for Quality Score Distortion in Reference-Based Alignment

The intrinsic high-entropy sequence metadata, known as quality scores, is largely the cause of the substantial size of sequence data files. Yet, there is no consensus on a viable reduction of the resolution of the quality score scale, arguably because of collateral side effects. In this article, we leverage on the penalty functions of HISAT2 aligner to rebin the quality score scale in such a way as to avoid any impact on sequence alignment, identifying alongside a distortion threshold for "safe" quality score representation. We tested our findings on whole-genome and RNA-seq data, and contrasted the results with three methods for lossy compression of the quality scores.

Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions

Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology.

Systematic benchmarking of omics computational tools

Computational omics methods packaged as software have become essential to modern biological research. The increasing dependence of scientists on these powerful software tools creates a need for systematic assessment of these methods, known as benchmarking. Adopting a standardized benchmarking practice could help researchers who use omics data to better leverage recent technological innovations. Our review summarizes benchmarking practices from 25 recent studies and discusses the challenges, advantages, and limitations of benchmarking across various domains of biology. We also propose principles that can make computational biology benchmarking studies more sustainable and reproducible, ultimately increasing the transparency of biomedical data and results.

Benchmarking studies are important for comprehensively understanding and evaluating different computational omics methods. Here, the authors review practices from 25 recent studies and propose principles to improve the quality of benchmarking studies.

Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings

Comprehensive and accurate detection of variants from whole-genome sequencing (WGS) is a strong prerequisite for translational genomic medicine; however, low concordance between analytic pipelines is an outstanding challenge. We processed a European and an African WGS samples with 70 analytic pipelines comprising the combination of 7 short-read aligners and 10 variant calling algorithms (VCAs), and observed remarkable differences in the number of variants called by different pipelines (max/min ratio: 1.3~3.4). The similarity between variant call sets was more closely determined by VCAs rather than by short-read aligners. Remarkably, reported minor allele frequency had a substantial effect on concordance between pipelines (concordance rate ratio: 0.11~0.92; Wald tests, P < 0.001), entailing more discordant results for rare and novel variants. We compared the performance of analytic pipelines and pipeline ensembles using gold-standard variant call sets and the catalog of variants from the 1000 Genomes Project. Notably, a single pipeline using BWA-MEM and GATK-HaplotypeCaller performed comparable to the pipeline ensembles for ‘callable’ regions (~97%) of the human reference genome. While a single pipeline is capable of analyzing common variants in most genomic regions, our findings demonstrated the limitations and challenges in analyzing rare or novel variants, especially for non-European genomes.

A multi-task convolutional deep neural network for variant calling in single molecule sequencing

The accurate identification of DNA sequence variants is an important, but challenging task in genomics. It is particularly difficult for single molecule sequencing, which has a per-nucleotide error rate of ~5-15%. Meeting this demand, we developed Clairvoyante, a multi-task five-layer convolutional neural network model for predicting variant type (SNP or indel), zygosity, alternative allele and indel length from aligned reads. For the well-characterized NA12878 human sample, Clairvoyante achieves 99.67, 95.78, 90.53% F1-score on 1KP common variants, and 98.65, 92.57, 87.26% F1-score for whole-genome analysis, using Illumina, PacBio, and Oxford Nanopore data, respectively. Training on a second human sample shows Clairvoyante is sample agnostic and finds variants in less than 2 h on a standard server. Furthermore, we present 3,135 variants that are missed using Illumina but supported independently by both PacBio and Oxford Nanopore reads. Clairvoyante is available open-source ( https://github.com/aquaskyline/Clairvoyante ), with modules to train, utilize and visualize the model.

In Vivo ChIP-Seq of Nuclear Receptors: A Rough Guide to Transform Frozen Tissues into High-Confidence Genome-Wide Binding Profiles

Chromatin immunoprecipitation coupled to next generation sequencing (ChIP-seq) is a powerful tool to map context-dependent genome-wide binding of nuclear hormone receptors and their coregulators. This information can provide important mechanistic insight into where, when and how DNA-protein interactions are linked to target gene regulation. Here we describe a simple, yet reliable ChIP-seq method, including nuclear isolation from frozen tissue samples, cross-linking DNA-protein complexes, chromatin shearing, immunoprecipitation, and purification of ChIP DNA. We also include a standard ChIP-seq data analysis pipeline to elaborate and analyze raw single-end or paired-end sequencing data, including quality control steps, peak calling, annotation, and motif enrichment.

Differential circRNA expression profiles in latent human cytomegalovirus infection and validation using clinical samples

Human cytomegalovirus (HCMV) is an opportunistic prototypic beta-herpesvirus that can cause severe and even fatal diseases in immune-naive newborns and immunocompromised adults. Host-virus interactions occurring at the transcriptional and posttranscriptional levels are critical for establishing an HCMV latent or lytic infection, but the mechanisms remain poorly understood. Herein, we investigated the expression of circRNAs in human leukemia monocytes (THP-1 cells) latently infected with HCMV and explored the diagnostic value of circRNAs in children with HCMV infection. A total of 2,110 and 1,912 circRNAs were identified in mock-infected and HCMV latent-infected THP-1 cells, respectively. Of these, we identified 1,421 differently expressed circRNAs, of which 650 were upregulated and 771 were downregulated. The host genes corresponding to the differentially expressed circRNAs were mainly involved in the regulation of host cell secretion pathways, cell cycle, and cell apoptosis. The differentially expressed circRNAs had binding sites for microRNAs, suggesting an important role in the mechanism of HCMV latent infection. Furthermore, a clinical analysis showed that the expression levels of hsa_circ_0001445 and hsa_circ_0001206 were statistically significantly different in HCMV-infected patients vs. normal controls, suggesting that these circRNAs could potentially serve as biomarkers of HCMV-infection.

Modelling of zero-inflation improves inference of metagenomic gene count data

Metagenomics enables the study of gene abundances in complex mixtures of microorganisms and has become a standard methodology for the analysis of the human microbiome. However, gene abundance data is inherently noisy and contains high levels of biological and technical variability as well as an excess of zeros due to non-detected genes. This makes the statistical analysis challenging. In this study, we present a new hierarchical Bayesian model for inference of metagenomic gene abundance data. The model uses a zero-inflated overdispersed Poisson distribution which is able to simultaneously capture the high gene-specific variability as well as zero observations in the data. By analysis of three comprehensive datasets, we show that zero-inflation is common in metagenomic data from the human gut and, if not correctly modelled, it can lead to substantial reductions in statistical power. We also show, by using resampled metagenomic data, that our model has, compared to other methods, a higher and more stable performance for detecting differentially abundant genes. We conclude that proper modelling of the gene-specific variability, including the excess of zeros, is necessary to accurately describe gene abundances in metagenomic data. The proposed model will thus pave the way for new biological insights into the structure of microbial communities.

Molecular profiling of subclinical inflammatory lesions in long-term surviving adult liver transplant recipients

BACKGROUND & AIMS

Subclinical inflammatory changes are commonly described in long-term transplant recipients undergoing protocol liver biopsies. The pathogenesis of these lesions remains unclear. The aim of this study was to identify the key molecular pathways driving progressive subclinical inflammatory liver allograft damage.

METHODS

All liver recipients followed at Hospital Clínic Barcelona who were >10 years post-transplant were screened for participation in the study. Patients with recurrence of underlying liver disease, biliary or vascular complications, chronic rejection, and abnormal liver function tests were excluded. Sixty-seven patients agreed to participate and underwent blood and serological tests, transient elastography and a liver biopsy. Transcriptome profiling was performed on RNA extracted from 49 out of the 67 biopsies employing a whole genome next generation sequencing platform. Patients were followed for a median of 6.8 years following the index liver biopsy.

RESULTS

Median time since transplantation to liver biopsy was 13 years (10-22). The most frequently observed histological abnormality was portal inflammation with different degrees of fibrosis, present in 45 biopsies (67%). Two modules of 102 and 425 co-expressed genes were significantly correlated with portal inflammation, interface hepatitis and portal fibrosis. These modules were enriched in molecular pathways known to be associated with T cell mediated rejection. Liver allografts showing the highest expression levels for the two modules recapitulated the transcriptional profile of biopsies with clinically apparent rejection and developed progressive damage over time, as assessed by non-invasive markers of fibrosis.

CONCLUSIONS

A large proportion of adult liver transplant recipients who survive long-term exhibit subclinical histological abnormalities. The transcriptomic profile of these patients' liver tissue closely resembles that of T cell mediated rejection and may result in progressive allograft damage.

LAY SUMMARY

A large proportion of adult liver transplant recipients who survive for a long time exhibit subclinical histological abnormalities. The expression profile (a measurement of the activity of genes) of liver tissue from a large fraction of these patients closely resembles the profile of T cell mediated rejection. Liver allografts showing the highest expression levels of rejection-related genes developed progressive damage over time.

GateKeeper: a new hardware architecture for accelerating pre-alignment in DNA short read mapping

Motivation

High throughput DNA sequencing (HTS) technologies generate an excessive number of small DNA segments -called short reads- that cause significant computational burden. To analyze the entire genome, each of the billions of short reads must be mapped to a reference genome based on the similarity between a read and 'candidate' locations in that reference genome. The similarity measurement, called alignment, formulated as an approximate string matching problem, is the computational bottleneck because: (i) it is implemented using quadratic-time dynamic programming algorithms and (ii) the majority of candidate locations in the reference genome do not align with a given read due to high dissimilarity. Calculating the alignment of such incorrect candidate locations consumes an overwhelming majority of a modern read mapper's execution time. Therefore, it is crucial to develop a fast and effective filter that can detect incorrect candidate locations and eliminate them before invoking computationally costly alignment algorithms.

Results

We propose GateKeeper, a new hardware accelerator that functions as a pre-alignment step that quickly filters out most incorrect candidate locations. GateKeeper is the first design to accelerate pre-alignment using Field-Programmable Gate Arrays (FPGAs), which can perform pre-alignment much faster than software. When implemented on a single FPGA chip, GateKeeper maintains high accuracy (on average >96%) while providing, on average, 90-fold and 130-fold speedup over the state-of-the-art software pre-alignment techniques, Adjacency Filter and Shifted Hamming Distance (SHD), respectively. The addition of GateKeeper as a pre-alignment step can reduce the verification time of the mrFAST mapper by a factor of 10.

Availability and implementation

https://github.com/BilkentCompGen/GateKeeper.

Contact

mohammedalser@bilkent.edu.tr or onur.mutlu@inf.ethz.ch or calkan@cs.bilkent.edu.tr.

Supplementary information

Supplementary data are available at Bioinformatics online.

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods

BACKGROUND

Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2.

RESULTS

To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations.

CONCLUSIONS

Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.

PriLive: privacy-preserving real-time filtering for next-generation sequencing

Motivation

In next-generation sequencing, re-identification of individuals and other privacy-breaching strategies can be applied even for anonymized data. This also holds true for applications in which human DNA is acquired as a by-product, e.g. for viral or metagenomic samples from a human host. Conventional data protection strategies including cryptography and post-hoc filtering are only appropriate for the final and processed sequencing data. This can result in an insufficient level of data protection and a considerable time delay in the further analysis workflow.

Results

We present PriLive, a novel tool for the automated removal of sensitive data while the sequencing machine is running. Thereby, human sequence information can be detected and removed before being completely produced. This facilitates the compliance with strict data protection regulations. The unique characteristic to cause almost no time delay for further analyses is also a clear benefit for applications other than data protection. Especially if the sequencing data are dominated by known background signals, PriLive considerably accelerates consequent analyses by having only fractions of input data. Besides these conceptual advantages, PriLive achieves filtering results at least as accurate as conventional post-hoc filtering tools.

Availability and implementation

PriLive is open-source software available at https://gitlab.com/rki_bioinformatics/PriLive.

Supplementary information

Supplementary data are available at Bioinformatics online.

miR-MaGiC improves quantification accuracy for small RNA-seq

Objective

Many tools have been developed to profile microRNA (miRNA) expression from small RNA-seq data. These tools must contend with several issues: the small size of miRNAs, the small number of unique miRNAs, the fact that similar miRNAs can be transcribed from multiple loci, and the presence of miRNA isoforms known as isomiRs. Methods failing to address these issues can return misleading information. We propose a novel quantification method designed to address these concerns.

Results

We present miR-MaGiC, a novel miRNA quantification method, implemented as a cross-platform tool in Java. miR-MaGiC performs stringent mapping to a core region of each miRNA and defines a meaningful set of target miRNA sequences by collapsing the miRNA space to “functional groups”. We hypothesize that these two features, mapping stringency and collapsing, provide more optimal quantification to a more meaningful unit (i.e., miRNA family). We test miR-MaGiC and several published methods on 210 small RNA-seq libraries, evaluating each method’s ability to accurately reflect global miRNA expression profiles. We define accuracy as total counts close to the total number of input reads originating from miRNAs. We find that miR-MaGiC, which incorporates both stringency and collapsing, provides the most accurate counts.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3418-2) contains supplementary material, which is available to authorized users.

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies

Background

Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments.

Results

We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x–6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x–3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm.

Conclusion

GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies

Next-Generation Sequencing (NGS) technologies are expected to play a crucial role in the surveillance of infectious diseases, with their unprecedented capabilities for the characterisation of genetic information underlying the virulence and antimicrobial resistance (AMR) properties of microorganisms.  In the implementation of any novel technology for regulatory purposes, important considerations such as harmonisation, validation and quality assurance need to be addressed.  NGS technologies pose unique challenges in these regards, in part due to their reliance on bioinformatics for the processing and proper interpretation of the data produced.  Well-designed benchmark resources are thus needed to evaluate, validate and ensure continued quality control over the bioinformatics component of the process.  This concept was explored as part of a workshop on "Next-generation sequencing technologies and antimicrobial resistance" held October 4-5 2017.   Challenges involved in the development of such a benchmark resource, with a specific focus on identifying the molecular determinants of AMR, were identified. For each of the challenges, sets of unsolved questions that will need to be tackled for them to be properly addressed were compiled. These take into consideration the requirement for monitoring of AMR bacteria in humans, animals, food and the environment, which is aligned with the principles of a “One Health” approach.