Adaptable probabilistic mapping of short reads using position specific scoring matrices

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.

Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners

Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.

ProDerAl: Reference Position Dependent Alignment

MOTIVATION

Current read-mapping software uses a singular specification of alignment parameters with respect to the reference. In the presence of varying reference structures (such as the repetitive regions of the human genome), alignments can be improved if those parameters are allowed vary.

RESULTS

To that end, the C ++ program ProDerAl was written to refine previously generated alignments using varying parameters for these problematic regions. Synthetic benchmarks show that this realignment can result in an order of magnitude fewer misaligned bases.

AVAILABILITY

*Nix users can retrieve the source from GitHub (https://github.com/Benjamin-Crysup/proderal.git). Windows binary available at https://github.com/Benjamin-Crysup/proderal/releases/download/v1.1/proderal.zip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using in silico predicted ancestral genomes to improve the efficiency of paleogenome reconstruction

Paleogenomics is the nascent discipline concerned with sequencing and analysis of genome-scale information from historic, ancient, and even extinct samples. While once inconceivable due to the challenges of DNA damage, contamination, and the technical limitations of PCR-based Sanger sequencing, following the dawn of the second-generation sequencing revolution, it has rapidly become a reality. However, a significant challenge facing ancient DNA studies on extinct species is the lack of closely related reference genomes against which to map the sequencing reads from ancient samples. Although bioinformatic efforts to improve the assemblies have focused mainly in mapping algorithms, in this article we explore the potential of an alternative approach, namely using reconstructed ancestral genome as reference for mapping DNA sequences of ancient samples. Specifically, we present a preliminary proof of concept for a general framework and demonstrate how under certain evolutionary divergence thresholds, considerable mapping improvements can be easily obtained.

Sensitive detection of circular DNAs at single-nucleotide resolution using guided realignment of partially aligned reads

BACKGROUND

Circular DNA has recently been identified across different species including human normal and cancerous tissue, but short-read mappers are unable to align many of the reads crossing circle junctions hence limiting their detection from short-read sequencing data.

RESULTS

Here, we propose a new method, Circle-Map that guides the realignment of partially aligned reads using information from discordantly mapped reads to map the short unaligned portions using a probabilistic model. We compared Circle-Map to similar up-to-date methods for circular DNA and RNA detection and we demonstrate how the approach implemented in Circle-Map dramatically increases sensitivity for detection of circular DNA on both simulated and real data while retaining high precision.

CONCLUSION

Circle-Map is an easy-to-use command line tool that implements the required pipeline to accurately detect circular DNA from circle enriched next generation sequencing experiments. Circle-Map is implemented in python3.6 and it is freely available at https://github.com/iprada/Circle-Map.

Combined microRNAome and transcriptome analysis of follicular phase and luteal phase in porcine ovaries

The aim of this study was to explore the expression difference of miRNAs and mRNAs between the follicular phase (FP) and luteal phase (LP) in porcine ovaries and provide a theoretical basis for the research on mammalian reproductive regulation. RNA-Seq and miRNA-Seq were used to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between the FP and LP in ovaries of six sows (3-year-old Yorkshire pigs with similar weights and same parities). Bioinformatic analysis was used to screen potential genes and miRNAs related to porcine ovarian function. Real-time qualitative PCR was used to validate the sequencing results. RNA-Seq results showed that 3,078 genes were up-regulated, and 1,444 genes were down-regulated in the LP compared with the FP, and DEGs were significantly enriched in 242 Gene Ontology (GO) terms and 33 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. miRNA-Seq identified 112 DEMs, of which 25 were up-regulated and 87 were down-regulated in the LP compared with the FP. We obtained 186 intersection genes (IGs) between the 4,522 DEGs and 2,444 target genes predicted from the 112 DEMs. After constructing a miRNA-gene-pathway network, we identified key miRNAs and genes including miR-17-3p, miR-214, miR-221-5p, miR-125b, FGF1, YWHAG, YWHAZ, FDFT1 and DHCR24, which are enriched in Hippo and PI3K-Akt signalling pathways, and various metabolic pathways. These results indicate that these key genes and miRNAs may play important roles in the developmental transition from FP to LP in porcine ovaries and represent candidate targets for further study.

Comparative analysis of differentially expressed genes between the ovaries from pregnant and nonpregnant goats using RNA-Seq

Background

A multitude of genes tightly regulate ovarian follicular development and hormone secretion. These complex and coordinated biological processes are altered during pregnancy. In order to further understand the regulatory role of these genes during pregnancy, it is important to screen the differentially expressed genes (DEGs) in the ovaries of pregnant and nonpregnant mammals. To detect the genes associated with the development of pregnancy in goats, DEGs from the ovaries from pregnant and nonpregnant Anhui white goats (pAWGs and nAWGs, respectively) were analyzed using RNA sequencing technology (RNA-Seq).

Results

In this study, 13,676,394 and 13,549,560 clean reads were generated from pAWGs and nAWGs, respectively, and 1724 DEGs were identified between the two libraries. Compared with nAWGs, 1033 genes were upregulated and 691 genes were downregulated in pAWGs, including PGR, PRLR, STAR and CYP19A1, which play important roles in goat reproduction. Gene Ontology analysis showed that the DEGs were enriched for 49 functional GO terms. Kyoto Encyclopedia of Genes and Genomes analysis revealed that 397 DEGs were significantly enriched in 13 pathways, including “cell cycle”, “cytokine–cytokine receptor interaction” and “steroid biosynthesis”, suggesting that the genes may be associated with cell cycle regulation, follicular development and hormone secretion to regulate the reproduction process. Additionally, quantitative real-time PCR was used to verify the reliability of the RNA-Seq data.

Conclusions

The data obtained in this work enrich the genetic resources of goat and provide a further understanding of the complex molecular regulatory mechanisms occurring during the development of pregnancy and reproduction in goats. The DEGs screened in this study may play an important role in follicular development and hormone secretion and they would provide scientific basis for related research in the future.

Electronic supplementary material

The online version of this article (10.1186/s40709-019-0095-9) contains supplementary material, which is available to authorized users.

Paleogenomics: reconstruction of plant evolutionary trajectories from modern and ancient DNA

How contemporary plant genomes originated and evolved is a fascinating question. One approach uses reference genomes from extant species to reconstruct the sequence and structure of their common ancestors over deep timescales. A second approach focuses on the direct identification of genomic changes at a shorter timescale by sequencing ancient DNA preserved in subfossil remains. Merged within the nascent field of paleogenomics, these complementary approaches provide insights into the evolutionary forces that shaped the organization and regulation of modern genomes and open novel perspectives in fostering genetic gain in breeding programs and establishing tools to predict future population changes in response to anthropogenic pressure and global warming.

Authentication and Assessment of Contamination in Ancient DNA

Contamination from both present-day humans and postmortem microbial sources is a common challenge in ancient DNA studies. Here we present a suite of tools to assist in the assessment of contamination in ancient DNA data sets. These tools perform standard tests of authenticity of ancient DNA data including detecting the presence of postmortem damage signatures in sequence alignments and quantifying the amount of present-day human contamination.

Comparative transcriptomic analysis of high and low egg-producing duck ovaries

The egg-laying rate is an important indicator of egg production of laying ducks. Egg production directly impacts the economic benefits of the duck industry. In order to obtain better insight into the molecular mechanisms associated with the process of egg production, comparative transcriptomic analysis of the ovaries of Jinding ducks with high and low egg production was performed using the Illumina HiSeq 2500 system. A total of 843 differentially expressed genes (DEGs) was identified, 367 that were down-regulated and 476 that were up-regulated in high egg production (HEP) ovaries, as compared with low egg production (LEP) ovaries. Some genes, such as MC5R, APOD, ORAI1, and DYRK4, were more active in HEP ovaries, indicating that these genes may play important roles in regulation of egg production. Among these 843 DEGs, 685 were assigned to gene ontology (GO) categories. Of these, 25 genes were related to reproduction, and 30 were related to the reproductive process, including some associated with ovarian follicle development, circadian regulation of gene expression, circadian rhythm, and estrogen receptor binding. Furthermore, some important functional pathways were revealed, such as the steroid biosynthesis pathway, the endocrine and other factor-regulated calcium reabsorption pathways, circadian rhythm, the neuroactive ligand-receptor interaction pathway, fatty acid biosynthesis, and the calcium-signaling pathway, which appear to be much more active in the HEP group, as compared to those of the LEP group. The results of this study provide very useful information that may contribute to future functional studies of genes involved in bird reproduction.

Hydrogen gas inhibits lung cancer progression through targeting SMC3

Lung cancer is one of the most common lethal malignancies in the globe. The patients' prognoses are dim due to its high metastatic potential and drug resistance. Therefore, in the present study, we aim to find a more potent therapeutic approach for lung cancer. We mainly explored the function of hydrogen gas (H₂) on cell viability, apoptosis, migration and invasion in lung cancer cell lines A549 and H1975 by CCK-8, flow cytometry, wound healing and transwell assays, respectively. We used RNA-seq, qPCR and western blotting to detect the different expression genes (DEGs) between H₂ group and control group to find the gene related to chromosome condensation. Besides, we confirmed the structural maintenance of chromosomes 3 (SMC3) and H₂ on the progression of lung cancer in vitro and vivo. Results showed that H₂ inhibited cell viability, migration and invasion, and catalyzed cell apoptosis and H₂ induced A549 and H1975 cells G2/M arrest. Besides, H₂ down-regulated the expression of NIBPL, SMC3, SMC5 and SMC6, and also reduced the expression of Cyclin D1, CDK4 and CDK6. H₂ translocated the subcellular location of SMC3 during cell division and decreased its stability and increased its ubiquitination in both A549 and H1975 cells. In addition, inhibition of the proliferation, migration and invasion and promotion of the apoptosis of A549 and H1975 cells induced by H₂ were all abolished when overexpressed SMC3 in the presence of H₂. Animal experimental assay demonstrated that the tumor weight in H₂ group was significantly smaller than that in control group, but was bigger than cis-platinum group. The expression of Ki-67, VEGF and SMC3 were decreased when mice were treated with H₂ or cis-platinum, especially for cis-platinum. All data suggested that H₂ inhibited lung cancer progression through down-regulating SMC3, a regulator for chromosome condensation, which provided a new method for the treatment of lung cancer.

Evolutionary Patterns and Processes: Lessons from Ancient DNA

Ever since its emergence in 1984, the field of ancient DNA has struggled to overcome the challenges related to the decay of DNA molecules in the fossil record. With the recent development of high-throughput DNA sequencing technologies and molecular techniques tailored to ultra-damaged templates, it has now come of age, merging together approaches in phylogenomics, population genomics, epigenomics, and metagenomics. Leveraging on complete temporal sample series, ancient DNA provides direct access to the most important dimension in evolution—time, allowing a wealth of fundamental evolutionary processes to be addressed at unprecedented resolution. This review taps into the most recent findings in ancient DNA research to present analyses of ancient genomic and metagenomic data.

Leukemia-propagating cells demonstrate distinctive gene expression profiles compared with other cell fractions from patients with de novo Philadelphia chromosome-positive ALL

Relapse remains one of the major obstacles in Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph⁺ALL) even after allogeneic hematopoietic stem cell transplantation. The persistence of leukemia-propagating cells (LPCs) may lead to the recurrence of Ph⁺ALL. Using a xenograft assay, LPCs enrichment in the CD34⁺CD38^-CD58^- fraction in Ph⁺ALL was recently identified. A further cohort study indicated that the LPCs phenotype at diagnosis was an independent risk factor for relapse of Ph⁺ALL. However, little is known about the potential molecular mechanism of LPCs-mediated relapse. Therefore, the gene expression profiles of the sorted LPCs and other cell fractions from patients with de novo Ph⁺ALL were investigated using RNA sequencing (RNA-Seq). Most of the differentially expressed genes between the LPCs and other cell fractions were related to the regulation of the cell cycle and metabolism, as identified by the gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Consistent with the RNA-Seq results, the mRNA levels of cell cycle-related genes, such as cyclin-dependent kinase 4, were significantly lower in the LPCs fraction than in other cell fractions. Moreover, the proportion of quiescent cells in LPCs was significantly higher than in other cell fractions. In summary, distinctive gene expression profiles and clusters, which were mostly related to the regulation of the cell cycle and metabolism, were demonstrated between LPCs and other cell fractions from patients with de novo Ph⁺ALL. Therefore, it would be beneficial to develop novel LPCs-based therapeutic strategies for Ph⁺ALL patients.

Systems analysis of phosphate-limitation-induced lipid accumulation by the oleaginous yeast Rhodosporidium toruloides

BACKGROUND

Lipid accumulation by oleaginous microorganisms is of great scientific interest and biotechnological potential. While nitrogen limitation has been routinely employed, low-cost raw materials usually contain rich nitrogenous components, thus preventing from efficient lipid production. Inorganic phosphate (Pi) limitation has been found sufficient to promote conversion of sugars into lipids, yet the molecular basis of cellular response to Pi limitation and concurrent lipid accumulation remains elusive.

RESULTS

Here, we performed multi-omic analyses of the oleaginous yeast Rhodosporidium toruloides to shield lights on Pi-limitation-induced lipid accumulation. Samples were prepared under Pi-limited as well as Pi-repleted chemostat conditions, and subjected to analysis at the transcriptomic, proteomic, and metabolomic levels. In total, 7970 genes, 4212 proteins, and 123 metabolites were identified. Results showed that Pi limitation facilitates up-regulation of Pi-associated metabolism, RNA degradation, and triacylglycerol biosynthesis while down-regulation of ribosome biosynthesis and tricarboxylic acid cycle. Pi limitation leads to dephosphorylation of adenosine monophosphate and the allosteric activator of isocitrate dehydrogenase key to lipid biosynthesis. It was found that NADPH, the key cofactor for fatty acid biosynthesis, is limited due to reduced flux through the pentose phosphate pathway and transhydrogenation cycle and that this can be overcome by over-expression of an endogenous malic enzyme. These phenomena are found distinctive from those under nitrogen limitation.

CONCLUSIONS

Our data suggest that Pi limitation activates Pi-related metabolism, RNA degradation, and TAG biosynthesis while inhibits ribosome biosynthesis and TCA cycle, leading to enhanced carbon fluxes into lipids. The information greatly enriches our understanding on microbial oleaginicity and Pi-related metabolism. Importantly, systems data may facilitate designing advanced cell factories for production of lipids and related oleochemicals.

Training alignment parameters for arbitrary sequencers with LAST-TRAIN

Summary

LAST-TRAIN improves sequence alignment accuracy by inferring substitution and gap scores that fit the frequencies of substitutions, insertions, and deletions in a given dataset. We have applied it to mapping DNA reads from IonTorrent and PacBio RS, and we show that it reduces reference bias for Oxford Nanopore reads.

Availability and Implementation

the source code is freely available at http://last.cbrc.jp/.

Contact

mhamada@waseda.jp or mcfrith@edu.k.u-tokyo.ac.jp.

Supplementary information

Supplementary data are available at Bioinformatics online.

Performance evaluation method for read mapping tool in clinical panel sequencing

In addition to the rapid advancement in Next-Generation Sequencing (NGS) technology, clinical panel sequencing is being used increasingly in clinical studies and tests. However, tools that are used in NGS data analysis have not been comparatively evaluated in performance for panel sequencing. This study aimed to evaluate the tools used in the alignment process, the first procedure in bioinformatics analysis, by comparing tools that have been widely used with ones that have been introduced recently. With the accumulated panel sequencing data, detected variant lists were cataloged and inserted into simulated reads produced from the reference genome (h19). The amount of unmapped reads and misaligned reads, mapping quality distribution, and runtime were measured as standards for comparison. As the most widely used tools, Bowtie2 and BWA–MEM each showed explicit performance with AUC of 0.9984 and 0.9970 respectively. Kart, maintaining superior runtime and less number of misaligned read, also similarly possessed high level of AUC (0.9723). Such selection and optimization method of tools appropriate for panel sequencing can be utilized for fields requiring error minimization, such as clinical application and liquid biopsy studies.

A Robust Framework for Microbial Archaeology

Microbial archaeology is flourishing in the era of high-throughput sequencing, revealing the agents behind devastating historical plagues, identifying the cryptic movements of pathogens in prehistory, and reconstructing the ancestral microbiota of humans. Here, we introduce the fundamental concepts and theoretical framework of the discipline, then discuss applied methodologies for pathogen identification and microbiome characterization from archaeological samples. We give special attention to the process of identifying, validating, and authenticating ancient microbes using high-throughput DNA sequencing data. Finally, we outline standards and precautions to guide future research in the field.

A Method for Next-Generation Sequencing of Paired Diagnostic and Remission Samples to Detect Mitochondrial DNA Mutations Associated with Leukemia

Somatic mitochondrial DNA (mtDNA) mutations have been identified in many human cancers, including leukemia. To identify somatic mutations, it is necessary to have a control tissue from the same individual for comparison. When patients with leukemia achieve remission, the remission peripheral blood may be a suitable and easily accessible control tissue, but this approach has not previously been applied to the study of mtDNA mutations. We have developed and validated a next-generation sequencing approach for the identification of leukemia-associated mtDNA mutations in 26 chronic myeloid leukemia patients at diagnosis using either nonhematopoietic or remission blood samples as the control. The entire mt genome was amplified by long-range PCR and sequenced using Illumina technology. Variant caller software was used to detect mtDNA somatic mutations, and an empirically determined threshold of 2% was applied to minimize false-positive results because of sequencing errors. Mutations were called against both nonhematopoietic and remission controls: the overall concordance between the two approaches was 81% (73/90 mutations). Some discordant results were because of the presence of somatic mutations in remission samples, because of either minimal residual disease or nonleukemic hematopoietic clones. This method could be applied to study somatic mtDNA mutations in leukemia patients who achieve minimal residual disease, and in patients with nonhematopoietic cancers who have a matched uninvolved tissue available.

Highly accessible AU-rich regions in 3' untranslated regions are hotspots for binding of regulatory factors

Post-transcriptional regulation is regarded as one of the major processes involved in the regulation of gene expression. It is mainly performed by RNA binding proteins and microRNAs, which target RNAs and typically affect their stability. Recent efforts from the scientific community have aimed at understanding post-transcriptional regulation at a global scale by using high-throughput sequencing techniques such as cross-linking and immunoprecipitation (CLIP), which facilitates identification of binding sites of these regulatory factors. However, the diversity in the experimental procedures and bioinformatics analyses has hindered the integration of multiple datasets and thus limited the development of an integrated view of post-transcriptional regulation. In this work, we have performed a comprehensive analysis of 107 CLIP datasets from 49 different RBPs in HEK293 cells to shed light on the complex interactions that govern post-transcriptional regulation. By developing a more stringent CLIP analysis pipeline we have discovered the existence of conserved regulatory AU-rich regions in the 3’UTRs where miRNAs and RBPs that regulate several processes such as polyadenylation or mRNA stability bind. Analogous to promoters, many factors have binding sites overlapping or in close proximity in these hotspots and hence the regulation of the mRNA may depend on their relative concentrations. This hypothesis is supported by RBP knockdown experiments that alter the relative concentration of RBPs in the cell. Upon AGO2 knockdown (KD), transcripts containing “free” target sites show increased expression levels compared to those containing target sites in hotspots, which suggests that target sites within hotspots are less available for miRNAs to bind. Interestingly, these hotspots appear enriched in genes with regulatory functions such as DNA binding and RNA binding. Taken together, our results suggest that hotspots are functional regulatory elements that define an extra layer of regulation of post-transcriptional regulatory networks.

All the cells in a given organism contain the same genome, yet their phenotype can be very diverse. The vast majority of this diversity arises from the differences in the expression of genes and proteins in them. One of the main mechanisms involved in controlling the protein and mRNA repertoire in cells is post-transcriptional regulation. The recent development of high-throughput sequencing techniques gives us now an unprecedented opportunity to investigate how post-transcriptional regulation works and which are the elements involved in defining the final set of mRNAs and proteins inside cells. In this work, we have performed a comprehensive computational analysis of several post-transcriptional regulators in a commonly used human cell line in order to understand which factors are involved in post-transcriptional regulation and how they coordinate their function. The results of our analysis show that this process is orchestrated around small regions in the mRNAs where many regulators bind and may compete with each other to regulate the mRNAs. The investigation and characterization of these regions gives us insight into the underlying combinatorial control that causes gene expression to differ across cell types and in diseases.

Morphological observation, RNA-Seq quantification, and expression profiling: novel insight into grafting-responsive carotenoid biosynthesis in watermelon grafted onto pumpkin rootstock

Watermelon is an important and economical horticultural crop in China, where ~20% of the plants are grafted. The development of grafted watermelon fruit involves a diverse range of gene interactions that results in dynamic changes in fruit. However, the molecular mechanisms underlying grafting-induced fruit quality change are unclear. In the present study, we measured the lycopene content by high-performance liquid chromatography and used RNA-Seq (quantification) to perform a genome-wide transcript analysis of fruits from watermelon grafted onto pumpkin rootstock (pumpkin-grafted watermelon, PGW), self-grafted watermelon (SGW), and non-grafted watermelon (NGW). The results showed variation in the lycopene content in the flesh of PGW fruits, first increasing and then decreasing in the four stages, which was different from the trend in the flesh of NGW and SGW fruits. The transcriptome profiling data provided new information on the grafting-induced gene regulation of lycopene biosynthesis during fruit growth and development. The expression levels of 33 genes from 8 gene families (GGPS, PSY, PDS, ZDS, CRTISO, LCYb, LCYe, and CHY) related to lycopene biosynthesis, which play critical roles in fruit coloration and contribute significantly to fruit phytonutrient values, were monitored during the four periods of fruit development in watermelon. Compared with those of NGW and SGW, 14 genes were differentially expressed in PGW during fruit development, suggesting that these genes possibly help to mediate lycopene biosynthesis in grafted watermelon fruit. Our work provides some novel insights into grafting-responsive carotenoid metabolism and its potential roles during PGW fruit development and ripening.

The PARA-suite: PAR-CLIP specific sequence read simulation and processing

Background

Next-generation sequencing technologies have profoundly impacted biology over recent years. Experimental protocols, such as photoactivatable ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP), which identifies protein–RNA interactions on a genome-wide scale, commonly employ deep sequencing. With PAR-CLIP, the incorporation of photoactivatable nucleosides into nascent transcripts leads to high rates of specific nucleotide conversions during reverse transcription. So far, the specific properties of PAR-CLIP-derived sequencing reads have not been assessed in depth.

Methods

We here compared PAR-CLIP sequencing reads to regular transcriptome sequencing reads (RNA-Seq) to identify distinctive properties that are relevant for reference-based read alignment of PAR-CLIP datasets. We developed a set of freely available tools for PAR-CLIP data analysis, called the PAR-CLIP analyzer suite (PARA-suite). The PARA-suite includes error model inference, PAR-CLIP read simulation based on PAR-CLIP specific properties, a full read alignment pipeline with a modified Burrows–Wheeler Aligner algorithm and CLIP read clustering for binding site detection.

Results

We show that differences in the error profiles of PAR-CLIP reads relative to regular transcriptome sequencing reads (RNA-Seq) make a distinct processing advantageous. We examine the alignment accuracy of commonly applied read aligners on 10 simulated PAR-CLIP datasets using different parameter settings and identified the most accurate setup among those read aligners. We demonstrate the performance of the PARA-suite in conjunction with different binding site detection algorithms on several real PAR-CLIP and HITS-CLIP datasets. Our processing pipeline allowed the improvement of both alignment and binding site detection accuracy.

Availability

The PARA-suite toolkit and the PARA-suite aligner are available at https://github.com/akloetgen/PARA-suite and https://github.com/akloetgen/PARA-suite_aligner, respectively, under the GNU GPLv3 license.

Identification and differential expression of microRNAs in the ovaries of pigs (Sus scrofa) with high and low litter sizes

Litter size affects profitability in the swine industry. Mammalian ovaries play important roles during reproduction, including ovulation and hormone secretion, which are tightly regulated by specific microRNAs (miRNAs). In this study, we investigated the effects of specific miRNAs on porcine litter size. We compared the ovarian miRNAs of Yorkshire pigs with high (YH) and low (YL) litter sizes using Solexa sequencing technology. We identified 327 and 320 miRNAs in the ovaries of YH and YL pigs respectively. A total of 297 miRNAs were co-expressed; 30 and 23 miRNAs respectively were specifically expressed in the two libraries. A total of 83 novel miRNAs were predicted; 37 specific miRNAs were obtained, of which 21 miRNAs were upregulated and 16 miRNAs were downregulated in YH compared with YL. Additionally, 19 628 and 19 250 target genes were predicted in the two libraries respectively. The results revealed that specific miRNAs (i.e., miR-224, miR-99a, let-7c, miR-181c, miR-214 and miR-21) may affect porcine litter size. The results of this study will help in gaining understanding of the role of miRNAs in porcine litter size regulation.

Toward high-resolution population genomics using archaeological samples

The term ‘ancient DNA’ (aDNA) is coming of age, with over 1,200 hits in the PubMed database, beginning in the early 1980s with the studies of ‘molecular paleontology’. Rooted in cloning and limited sequencing of DNA from ancient remains during the pre-PCR era, the field has made incredible progress since the introduction of PCR and next-generation sequencing. Over the last decade, aDNA analysis ushered in a new era in genomics and became the method of choice for reconstructing the history of organisms, their biogeography, and migration routes, with applications in evolutionary biology, population genetics, archaeogenetics, paleo-epidemiology, and many other areas. This change was brought by development of new strategies for coping with the challenges in studying aDNA due to damage and fragmentation, scarce samples, significant historical gaps, and limited applicability of population genetics methods. In this review, we describe the state-of-the-art achievements in aDNA studies, with particular focus on human evolution and demographic history. We present the current experimental and theoretical procedures for handling and analysing highly degraded aDNA. We also review the challenges in the rapidly growing field of ancient epigenomics. Advancement of aDNA tools and methods signifies a new era in population genetics and evolutionary medicine research.

A Pipeline for PAR-CLIP Data Analysis

Photo-activatable ribonucleoside cross-linking and immunoprecipitation (PAR-CLIP) is a method to detect binding sites of RNA-binding proteins (RBPs) transcriptome-wide. This chapter covers the computational analysis of the high-throughput sequencing reads generated from PAR-CLIP experiments. It explains how the reads are mutated due to UV cross-linking and how to appropriately pre-process and align them to a reference sequence. Aligned reads are then aggregated into clusters which represent putative RBP-binding sites. Mapping artifacts are a source of false positives, which can be controlled by means of a mapping decoy and adaptive quality filtering of the read clusters. A step-by-step explanation of this procedure is given. All necessary tools are open source, including the scripts presented and used in this chapter.

Comparison of Sample Preparation Methods Used for the Next-Generation Sequencing of Mycobacterium tuberculosis

The advent and widespread application of next-generation sequencing (NGS) technologies to the study of microbial genomes has led to a substantial increase in the number of studies in which whole genome sequencing (WGS) is applied to the analysis of microbial genomic epidemiology. However, microorganisms such as Mycobacterium tuberculosis (MTB) present unique problems for sequencing and downstream analysis based on their unique physiology and the composition of their genomes. In this study, we compare the quality of sequence data generated using the Nextera and TruSeq isolate preparation kits for library construction prior to Illumina sequencing-by-synthesis. Our results confirm that MTB NGS data quality is highly dependent on the purity of the DNA sample submitted for sequencing and its guanine-cytosine content (or GC-content). Our data additionally demonstrate that the choice of library preparation method plays an important role in mitigating downstream sequencing quality issues. Importantly for MTB, the Illumina TruSeq library preparation kit produces more uniform data quality than the Nextera XT method, regardless of the quality of the input DNA. Furthermore, specific genomic sequence motifs are commonly missed by the Nextera XT method, as are regions of especially high GC-content relative to the rest of the MTB genome. As coverage bias is highly undesirable, this study illustrates the importance of appropriate protocol selection when performing NGS studies in order to ensure that sound inferences can be made regarding mycobacterial genomes.

Transcriptomic Analysis of Ovaries from Pigs with High And Low Litter Size

Litter size is one of the most important economic traits for pig production as it is directly related to the production efficiency. Litter size is affected by interactions between multiple genes and the environment. While recent studies have identified some genes associated with prolificacy in pigs, transcriptomic studies of specific genes affecting litter size in porcine ovaries are rare. In order to identify candidate genes associated with litter size in swine, we assessed gene expression differences between the ovaries of Yorkshire pigs with extremely high and low litter sizes using the RNA-Seq method. A total of 1 243 differentially expressed genes were identified: 897 genes were upregulated and 346 genes were downregulated in high litter size ovary samples compared with low litter size ovary samples. A large number of these genes related to steroid hormone regulation in animal ovaries, including 59 Gene Ontology terms and 27 Kyoto Encyclopedia of Genes and Genomes pathways involved in steroid biosynthesis and ovarian steroidogenesis. From these differentially expressed genes, we identified a total of 11 genes using a bioinformatics screen that may be associated with high litter size in Yorkshire pigs. These results provide a list of new candidate genes for porcine litter size and prolificacy to be further investigated.

Reconstructing ancient genomes and epigenomes

Research involving ancient DNA (aDNA) has experienced a true technological revolution in recent years through advances in the recovery of aDNA and, particularly, through applications of high-throughput sequencing. Formerly restricted to the analysis of only limited amounts of genetic information, aDNA studies have now progressed to whole-genome sequencing for an increasing number of ancient individuals and extinct species, as well as to epigenomic characterization. Such advances have enabled the sequencing of specimens of up to 1 million years old, which, owing to their extensive DNA damage and contamination, were previously not amenable to genetic analyses. In this Review, we discuss these varied technical challenges and solutions for sequencing ancient genomes and epigenomes.

Impacts of low coverage depths and post-mortem DNA damage on variant calling: a simulation study

Background

Massively parallel sequencing platforms, featuring high throughput and relatively short read lengths, are well suited to ancient DNA (aDNA) studies. Variant identification from short-read alignment could be hindered, however, by low DNA concentrations common to historic samples, which constrain sequencing depths, and post-mortem DNA damage patterns.

Results

We simulated pairs of sequences to act as reference and sample genomes at varied GC contents and divergence levels. Short-read sequence pools were generated from sample sequences, and subjected to varying levels of “post-mortem” damage by adjusting levels of fragmentation and fragmentation biases, transition rates at sequence ends, and sequencing depths. Mapping of sample read pools to reference sequences revealed several trends, including decreased alignment success with increased read length and decreased variant recovery with increased divergence. Variants were generally called with high accuracy, however identification of SNPs (single-nucleotide polymorphisms) was less accurate for high damage/low divergence samples. Modest increases in sequencing depth resulted in rapid gains in total variant recovery, and limited improvements to recovery of heterozygous variants.

Conclusions

This in silico study suggests aDNA-associated damage patterns minimally impact variant call accuracy and recovery from short-read alignment, while modest increases in sequencing depth can greatly improve variant recovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1219-8) contains supplementary material, which is available to authorized users.

Bioinformatic analysis of cis-regulatory interactions between progesterone and estrogen receptors in breast cancer

Chromatin factors interact with each other in a cell and sequence-specific manner in order to regulate transcription and a wealth of publically available datasets exists describing the genomic locations of these interactions. Our recently published BiSA (Binding Sites Analyser) database contains transcription factor binding locations and epigenetic modifications collected from published studies and provides tools to analyse stored and imported data. Using BiSA we investigated the overlapping cis-regulatory role of estrogen receptor alpha (ERα) and progesterone receptor (PR) in the T-47D breast cancer cell line. We found that ERα binding sites overlap with a subset of PR binding sites. To investigate further, we re-analysed raw data to remove any biases introduced by the use of distinct tools in the original publications. We identified 22,152 PR and 18,560 ERα binding sites (<5% false discovery rate) with 4,358 overlapping regions among the two datasets. BiSA statistical analysis revealed a non-significant overall overlap correlation between the two factors, suggesting that ERα and PR are not partner factors and do not require each other for binding to occur. However, Monte Carlo simulation by Binary Interval Search (BITS), Relevant Distance, Absolute Distance, Jaccard and Projection tests by Genometricorr revealed a statistically significant spatial correlation of binding regions on chromosome between the two factors. Motif analysis revealed that the shared binding regions were enriched with binding motifs for ERα, PR and a number of other transcription and pioneer factors. Some of these factors are known to co-locate with ERα and PR binding. Therefore spatially close proximity of ERα binding sites with PR binding sites suggests that ERα and PR, in general function independently at the molecular level, but that their activities converge on a specific subset of transcriptional targets.

SNPest: a probabilistic graphical model for estimating genotypes

Background

As the use of next-generation sequencing technologies is becoming more widespread, the need for robust software to help with the analysis is growing as well. A key challenge when analyzing sequencing data is the prediction of genotypes from the reads, i.e. correct inference of the underlying DNA sequences that gave rise to the sequenced fragments. For diploid organisms, the genotyper should be able to predict both alleles in the individual. Variations between the individual and the population can then be analyzed by looking for SNPs (single nucleotide polymorphisms) in order to investigate diseases or phenotypic features. To perform robust and high confidence genotyping and SNP calling, methods are needed that take the technology specific limitations into account and can model different sources of error. As an example, ancient DNA poses special challenges as the data is often shallow and subject to errors induced by post mortem damage.

Findings

We present a novel approach to the genotyping problem where a probabilistic framework describing the process from sampling to sequencing is implemented as a graphical model. This makes it possible to model technology specific errors and other sources of variation that can affect the result. The inferred genotype is given a posterior probability to signify the confidence in the result. SNPest has already been used to genotype large scale projects such as the first ancient human genome published in 2010.

Conclusions

We compare the performance of SNPest to a number of other widely used genotypers on both real and simulated data, covering both haploid and diploid genomes. We investigate the effects of read depth, of removing adapters before mapping and genotyping, of using different mapping tools, and of using the correct model in the genotyping process. We show that the performance of SNPest is comparable to existing methods, and we also illustrate cases where SNPest has an advantage over other methods, e.g. when dealing with simulated ancient DNA.

Electronic supplementary material

The online version of this article (doi:10.1186/1756-0500-7-698) contains supplementary material, which is available to authorized users.

On the accuracy of short read mapping

The development of high-throughput sequencing technologies has revolutionized the way we study genomes and gene regulation. In a single experiment, millions of reads are produced. To gain knowledge from these experiments the first thing to be done is finding the genomic origin of the reads, i.e., mapping the reads to a reference genome. In this new situation, conventional alignment tools are obsolete, as they cannot handle this huge amount of data in a reasonable amount of time. Thus, new mapping algorithms have been developed, which are fast at the expense of a small decrease in accuracy. In this chapter we discuss the current problems in short read mapping and show that mapping reads correctly is a nontrivial task. Through simple experiments with both real and synthetic data, we demonstrate that different mappers can give different results depending on the type of data, and that a considerable fraction of uniquely mapped reads is potentially mapped to an incorrect location. Furthermore, we provide simple statistical results on the expected number of random matches in a genome (E-value) and the probability of a random match as a function of read length. Finally, we show that quality scores contain valuable information for mapping and why mapping quality should be evaluated in a probabilistic manner. In the end, we discuss the potential of improving the performance of current methods by considering these quality scores in a probabilistic mapping program.