SEED: efficient clustering of next-generation sequences

GradHC: highly reliable gradual hash-based clustering for DNA storage systems

MOTIVATION

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands.

RESULTS

In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results.

AVAILABILITY AND IMPLEMENTATION

https://github.com/bensdvir/GradHC.

Clover: tree structure-based efficient DNA clustering for DNA-based data storage

Deoxyribonucleic acid (DNA)-based data storage is a promising new storage technology which has the advantage of high storage capacity and long storage time compared with traditional storage media. However, the synthesis and sequencing process of DNA can randomly generate many types of errors, which makes it more difficult to cluster DNA sequences to recover DNA information. Currently, the available DNA clustering algorithms are targeted at DNA sequences in the biological domain, which not only cannot adapt to the characteristics of sequences in DNA storage, but also tend to be unacceptably time-consuming for billions of DNA sequences in DNA storage. In this paper, we propose an efficient DNA clustering method termed Clover for DNA storage with linear computational complexity and low memory. Clover avoids the computation of the Levenshtein distance by using a tree structure for interval-specific retrieval. We argue through theoretical proofs that Clover has standard linear computational complexity, low space complexity, etc. Experiments show that our method can cluster 10 million DNA sequences into 50 000 classes in 10 s and meet an accuracy rate of over 99%. Furthermore, we have successfully completed an unprecedented clustering of 10 billion DNA data on a single home computer and the time consumption still satisfies the linear relationship. Clover is freely available at https://github.com/Guanjinqu/Clover.

Analysis of Faecal Microbiota and Small ncRNAs in Autism: Detection of miRNAs and piRNAs with Possible Implications in Host-Gut Microbiota Cross-Talk

Intestinal microorganisms impact health by maintaining gut homeostasis and shaping the host immunity, while gut dysbiosis associates with many conditions, including autism, a complex neurodevelopmental disorder with multifactorial aetiology. In autism, gut dysbiosis correlates with symptom severity and is characterised by a reduced bacterial variability and a diminished beneficial commensal relationship. Microbiota can influence the expression of host microRNAs that, in turn, regulate the growth of intestinal bacteria by means of bidirectional host-gut microbiota cross-talk. We investigated possible interactions among intestinal microbes and between them and host transcriptional modulators in autism. To this purpose, we analysed, by "omics" technologies, faecal microbiome, mycobiome, and small non-coding-RNAs (particularly miRNAs and piRNAs) of children with autism and neurotypical development. Patients displayed gut dysbiosis related to a reduction of healthy gut micro- and mycobiota as well as up-regulated transcriptional modulators. The targets of dysregulated non-coding-RNAs are involved in intestinal permeability, inflammation, and autism. Furthermore, microbial families, underrepresented in patients, participate in the production of human essential metabolites negatively influencing the health condition. Here, we propose a novel approach to analyse faeces as a whole, and for the first time, we detected miRNAs and piRNAs in faecal samples of patients with autism.

De Novo Clustering of Long-Read Transcriptome Data Using a Greedy, Quality Value-Based Algorithm

Long-read sequencing of transcripts with Pacific Biosciences (PacBio) Iso-Seq and Oxford Nanopore Technologies has proven to be central to the study of complex isoform landscapes in many organisms. However, current de novo transcript reconstruction algorithms from long-read data are limited, leaving the potential of these technologies unfulfilled. A common bottleneck is the dearth of scalable and accurate algorithms for clustering long reads according to their gene family of origin. To address this challenge, we develop isONclust, a clustering algorithm that is greedy (to scale) and makes use of quality values (to handle variable error rates). We test isONclust on three simulated and five biological data sets, across a breadth of organisms, technologies, and read depths. Our results demonstrate that isONclust is a substantial improvement over previous approaches, both in terms of overall accuracy and/or scalability to large data sets.

MeShClust: an intelligent tool for clustering DNA sequences

Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClust's ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.

Using BEAN-counter to quantify genetic interactions from multiplexed barcode sequencing experiments

The construction of genome-wide mutant collections has enabled high-throughput, high-dimensional quantitative characterization of gene and chemical function, particularly via genetic and chemical-genetic interaction experiments. As the throughput of such experiments increases with improvements in sequencing technology and sample multiplexing, appropriate tools must be developed to handle the large volume of data produced. Here, we describe how to apply our approach to high-throughput, fitness-based profiling of pooled mutant yeast collections using the BEAN-counter software pipeline (Barcoded Experiment Analysis for Next-generation sequencing) for analysis. The software has also successfully processed data from Schizosaccharomyces pombe, Escherichia coli, and Zymomonas mobilis mutant collections. We provide general recommendations for the design of large-scale, multiplexed barcode sequencing experiments. The procedure outlined here was used to score interactions for ~4 million chemical-by-mutant combinations in our recently published chemical-genetic interaction screen of nearly 14,000 chemical compounds across seven diverse compound collections. Here we selected a representative subset of these data on which to demonstrate our analysis pipeline. BEAN-counter is open source, written in Python, and freely available for academic use. Users should be proficient at the command line; advanced users who wish to analyze larger datasets with hundreds or more conditions should also be familiar with concepts in analysis of high-throughput biological data. BEAN-counter encapsulates the knowledge we have accumulated from, and successfully applied to, our multiplexed, pooled barcode sequencing experiments. This protocol will be useful to those interested in generating their own high-dimensional, quantitative characterizations of gene or chemical function in a high-throughput manner.

De novo clustering of long reads by gene from transcriptomics data

Long-read sequencing currently provides sequences of several thousand base pairs. It is therefore possible to obtain complete transcripts, offering an unprecedented vision of the cellular transcriptome. However the literature lacks tools for de novo clustering of such data, in particular for Oxford Nanopore Technologies reads, because of the inherent high error rate compared to short reads. Our goal is to process reads from whole transcriptome sequencing data accurately and without a reference genome in order to reliably group reads coming from the same gene. This de novo approach is therefore particularly suitable for non-model species, but can also serve as a useful pre-processing step to improve read mapping. Our contribution both proposes a new algorithm adapted to clustering of reads by gene and a practical and free access tool that allows to scale the complete processing of eukaryotic transcriptomes. We sequenced a mouse RNA sample using the MinION device. This dataset is used to compare our solution to other algorithms used in the context of biological clustering. We demonstrate that it is the best approach for transcriptomics long reads. When a reference is available to enable mapping, we show that it stands as an alternative method that predicts complementary clusters.

Bartender: a fast and accurate clustering algorithm to count barcode reads

Motivation

Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.

Results

In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.

Availability and implementation

Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.

Contact

sasha.levy@stonybrook.edu or song.wu@stonybrook.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

An efficient classification algorithm for NGS data based on text similarity

With the advancement of high-throughput sequencing technologies, the amount of available sequencing data is growing at a pace that has now begun to greatly challenge the data processing and storage capacities of modern computer systems. Removing redundancy from such data by clustering could be crucial for reducing memory, disk space and running time consumption. In addition, it also has good performance on reducing dataset noise in some analysis applications. In this study, we propose a high-performance short sequence classification algorithm (HSC) for next generation sequencing (NGS) data based on efficient hash function and text similarity. First, HSC converts all reads into k-mers, then it forms a unique k-mer set by merging the duplicated and reverse complementary elements. Second, all unique k-mers are stored in a hash table, where the k-mer string is stored in the key field, and the ID of the reads containing the k-mer are stored in the value field. Third, each hash unit is transformed into a short text consisting of reads. Fourth, texts that satisfy the similarity threshold are combined into a long text, the merge operation is executed iteratively until there is no text that satisfies the merge condition. Finally, the long text is transformed into a cluster consisting of reads. We tested HSC using five real datasets. The experimental results showed that HSC cluster 100 million short reads within 2 hours, and it has excellent performance in reducing memory consumption. Compared to existing methods, HSC is much faster than other tools, it can easily handle tens of millions of sequences. In addition, when HSC is used as a preprocessing tool to produce assembly data, the memory and time consumption of the assembler is greatly reduced. It can help the assembler to achieve better assemblies in terms of N50, NA50 and genome fraction.

A pipeline for metabarcoding and diet analysis from fecal samples developed for a small semi-aquatic mammal

Metabarcoding allows the genetic analysis of pooled samples of various sources. It is becoming popular in the study of animal diet, especially because it allows the analysis of the composition of feces without the need of handling animals. In this work, we studied the diet of the Pyrenean desman (Galemys pyrenaicus), a small semi-aquatic mammal endemic to the Iberian Peninsula and the Pyrenees, by sequencing COI minibarcodes from feces using next-generation sequencing techniques. For the identification of assembled sequences, we employed a tree-based identification method that used a reference tree of sequences of freshwater organisms. The comparison of freshly collected fecal samples and older samples showed that fresh samples produced significantly more sequencing reads. They also rendered more operational taxonomical units (OTUs), but not significantly. Our analyses of 41 samples identified 224 OTUs corresponding to species of the reference tree. Ephemeroptera, Diptera excl. Chironomidae, and Chironomidae were the most highly represented groups in terms of reads as well as samples. Other groups of freshwater organisms detected were Plecoptera, Trichoptera, Neuropteroida, Coleoptera, Crustacea, and Annelida. Our results are largely in line with previous morphological and genetic studies on the diet of the Pyrenean desman, but allowed the identification of a higher diversity of OTUs in each sample. Additionally, the bioinformatic pipeline we developed for deep sequencing of fecal samples will enable the quantitative analysis of the diet of this and other species, which can be highly useful to determine their ecological requirements.

Sequence analysis of the potato aphid Macrosiphum euphorbiae transcriptome identified two new viruses

The potato aphid, Macrosiphum euphorbiae, is an important agricultural pest that causes economic losses to potato and tomato production. To establish the transcriptome for this aphid, RNA-Seq libraries constructed from aphids maintained on tomato plants were used in Illumina sequencing generating 52.6 million 75-105 bp paired-end reads. The reads were assembled using Velvet/Oases software with SEED preprocessing resulting in 22,137 contigs with an N50 value of 2,003bp. After removal of contigs from tomato host origin, 20,254 contigs were annotated using BLASTx searches against the non-redundant protein database from the National Center for Biotechnology Information (NCBI) as well as IntereProScan. This identified matches for 74% of the potato aphid contigs. The highest ranking hits for over 12,700 contigs were against the related pea aphid, Acyrthosiphon pisum. Gene Ontology (GO) was used to classify the identified M. euphorbiae contigs into biological process, cellular component and molecular function. Among the contigs, sequences of microbial origin were identified. Sixty five contigs were from the aphid bacterial obligate endosymbiont Buchnera aphidicola origin and two contigs had amino acid similarities to viruses. The latter two were named Macrosiphum euphorbiae virus 2 (MeV-2) and Macrosiphum euphorbiae virus 3 (MeV-3). The highest sequence identity to MeV-2 had the Dysaphis plantaginea densovirus, while to MeV-3 is the Hubei sobemo-like virus 49. Characterization of MeV-2 and MeV-3 indicated that both are transmitted vertically from adult aphids to nymphs. MeV-2 peptides were detected in the aphid saliva and only MeV-2 and not MeV-3 nucleic acids were detected inside tomato leaves exposed to virus-infected aphids. However, MeV-2 nucleic acids did not persist in tomato leaf tissues, after clearing the plants from aphids, indicating that MeV-2 is likely an aphid virus.

Harvesting Environmental Microalgal Blooms for Remediation and Resource Recovery: A Laboratory Scale Investigation with Economic and Microbial Community Impact Assessment

A laboratory based microflotation rig termed efficient FLOtation of Algae Technology (eFLOAT) was used to optimise parameters for harvesting microalgal biomass from eutrophic water systems. This was performed for the dual objectives of remediation (nutrient removal) and resource recovery. Preliminary experiments demonstrated that chitosan was more efficient than alum for flocculation of biomass and the presence of bacteria could play a positive role and reduce flocculant application rates under the natural conditions tested. Maximum biomass removal from a hyper-eutrophic water retention pond sample was achieved with 5 mg·L^-1 chitosan (90% Chlorophyll a removal). Harvesting at maximum rates showed that after 10 days, the bacterial diversity is significantly increased with reduced cyanobacteria, indicating improved ecosystem functioning. The resource potential within the biomass was characterized by 9.02 μg phosphate, 0.36 mg protein, and 103.7 μg lipid per mg of biomass. Fatty acid methyl ester composition was comparable to pure cultures of microalgae, dominated by C16 and C18 chain lengths with saturated, monounsaturated, and polyunsaturated fatty acids. Finally, the laboratory data was translated into a full-size and modular eFLOAT system, with estimated costs as a novel eco-technology for efficient algal bloom harvesting.

High taxonomic diversity of cultivation-recalcitrant endophytic bacteria in grapevine field shoots, their in vitro introduction, and unsuspected persistence

Molecular and microscopic analyses reveal enormous non-cultivable endophytic bacteria in grapevine field shoots with functional significance. Diverse bacteria enter tissue cultures through surface-sterilized tissues and survive surreptitiously with varying taxonomic realignments. The study was envisaged to assess the extent of endophytic bacterial association with field shoot tissues of grapevine and the likelihood of introduction of such internally colonizing bacteria in vitro adopting molecular techniques targeting the non-cultivable bacterial community. PowerFood^®-kit derived DNA from surface-sterilized field shoot tips of grapevine Flame Seedless was employed in a preliminary bacterial class-specific PCR screening proving positive for major prokaryotic taxa including Archaea. Taxonomic and functional diversity were analyzed through whole metagenome profiling (WMG) which revealed predominantly phylum Actinobacteria, Proteobacteria, and minor shares of Firmicutes, Bacteroidetes, and Deinococcus-Thermus with varying functional roles ascribable to the whole bacterial community. Field shoot tip tissues and callus derived from stem segments were further employed in 16S rRNA V3-V4 amplicon taxonomic profiling. This revealed elevated taxonomic diversity in field shoots over WMG, predominantly Proteobacteria succeeded by Actinobacteria, Firmicutes, Bacteroidetes, and 15 other phyla including several candidate phyla (135 families, 179 genera). Callus stocks also displayed broad bacterial diversity (16 phyla; 96 families; 141 genera) bearing resemblance to field tissues with Proteobacterial dominance but a reduction in its share, enrichment of Actinobacteria and Firmicutes, disappearance of some field-associated phyla and detection of a few additional taxonomic groups over field community. Similar results were documented during 16S V3-V4 amplicon taxonomic profiling on Thompson Seedless field shoot tip and callus tissues. Video microscopy on tissue homogenates corroborated enormous endophytic bacteria. This study elucidates a vast diversity of cultivation-recalcitrant endophytic bacteria prevailing in grapevine field shoots, their in vitro introduction, and unsuspecting sustenance with possible silent participation in tissue culture processes.

pBACode: a random-barcode-based high-throughput approach for BAC paired-end sequencing and physical clone mapping

Applications that use Bacterial Artificial Chromosome (BAC) libraries often require paired-end sequences and knowledge of the physical location of each clone in plates. To facilitate obtaining this information in high-throughput, we generated pBACode vectors: a pool of BAC cloning vectors, each with a pair of random barcodes flanking its cloning site. In a pBACode BAC library, the BAC ends and their linked barcodes can be sequenced in bulk. Barcode pairs are determined by sequencing the empty pBACode vectors, which allows BAC ends to be paired according to their barcodes. For physical clone mapping, the barcodes are used as unique markers for their linked genomic sequence. After multi-dimensional pooling of BAC clones, the barcodes are sequenced and deconvoluted to locate each clone. We generated a pBACode library of 94,464 clones for the flounder Paralichthys olivaceus and obtained paired-end sequence from 95.4% of the clones. Incorporating BAC paired-ends into the genome preassembly improved its continuity by over 10-fold. Furthermore, we were able to use the barcodes to map the physical locations of each clone in just 50 pools, with up to 11 808 clones per pool. Our physical clone mapping located 90.2% of BAC clones, enabling targeted characterization of chromosomal rearrangements.

Complete Genome Sequence of Bacillus velezensis CBMB205, a Phosphate-Solubilizing Bacterium Isolated from the Rhizoplane of Rice in the Republic of Korea

Bacillus velezensis CBMB205 (= KACC 13105^T = NCCB 100236^T) was isolated from the rhizoplane of rice (Oryza sativa L. cv. O-dae). According to previous studies, this bacterium has several genes that can promote plant growth, such as the phosphorus-solubilizing protein-coding gene. Here, we present the first complete genome of B. velezensis CBMB205.

Complete Genome Sequence of Dyella thiooxydans ATSB10, a Thiosulfate-Oxidizing Bacterium Isolated from Sunflower Fields in South Korea

Dyella thiooxydans ATSB10 (KACC 12756^T = LMG 24673^T) is a thiosulfate-oxidizing bacterium isolated from rhizosphere soils of sunflower plants. In this study, we completely sequenced the genome of D. thiooxydans ATSB10 and identified the genes involved in thiosulfate oxidation and the metabolism of aromatic intermediates.

A novel virus from Macrosiphum euphorbiae with similarities to members of the family Flaviviridae

A virus with a large genome was identified in the transcriptome of the potato aphid (Macrosiphum euphorbiae) and was named Macrosiphum euphorbiae virus 1 (MeV-1). The MeV-1 genome is 22 780 nt in size, including 3' and 5' non-coding regions, with a single large ORF encoding a putative polyprotein of 7333 aa. The C-terminal region of the predicted MeV-1 polyprotein contained sequences with similarities to helicase, methyltransferase and RNA-dependent RNA polymerase (RdRp) motifs, while the N-terminal region lacked any motifs including structural proteins. Phylogenetic analysis of the helicase placed MeV-1 close to pestiviruses, while the RdRp region placed it close to pestiviruses and flaviviruses, suggesting MeV-1 has a positive-polarity ssRNA genome and is a member of the family Flaviviridae. Since the MeV-1 genome is predicted to contain a methyltransferase, a gene present typically in flaviviruses but not pestiviruses, MeV-1 is likely a member of the genus Flavivirus. MeV-1 was present in nymphal and adult stages of the aphid, aphid saliva and plant tissues fed upon by aphids. However, the virus was unable to multiply and spread in tomato plants. In addition, dsRNA, the replication intermediate of RNA viruses, was isolated from virus-infected M. euphorbiae and not from tomato plants infested with the aphid. Furthermore, nymphs laid without exposure to infected plants harboured the virus, indicating that MeV-1 is an aphid-infecting virus likely transmitted transovarially. The virus was present in M. euphorbiae populations from Europe but not from North America and was absent in all other aphid species tested.

Starcode: sequence clustering based on all-pairs search

Motivation: The increasing throughput of sequencing technologies offers new applications and challenges for computational biology. In many of those applications, sequencing errors need to be corrected. This is particularly important when sequencing reads from an unknown reference such as random DNA barcodes. In this case, error correction can be done by performing a pairwise comparison of all the barcodes, which is a computationally complex problem.

Results: Here, we address this challenge and describe an exact algorithm to determine which pairs of sequences lie within a given Levenshtein distance. For error correction or redundancy reduction purposes, matched pairs are then merged into clusters of similar sequences. The efficiency of starcode is attributable to the poucet search, a novel implementation of the Needleman–Wunsch algorithm performed on the nodes of a trie. On the task of matching random barcodes, starcode outperforms sequence clustering algorithms in both speed and precision.

Availability and implementation: The C source code is available at http://github.com/gui11aume/starcode.

Contact:guillaume.filion@gmail.com

Clustering of reads with alignment-free measures and quality values

Background

The data volume generated by Next-Generation Sequencing (NGS) technologies is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads.

Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %).

Results

In this scenario it will be fundamental to exploit quality value information within the alignment-free framework. To the best of our knowledge this is the first study that incorporates quality value information and k-mers counts, in the context of alignment-free measures, for the comparison of reads data. Based on this principles, in this paper we present a family of alignment-free measures called D^q-type. A set of experiments on simulated and real reads data confirms that the new measures are superior to other classical alignment-free statistics, especially when erroneous reads are considered. Also results on de novo assembly and metagenomic reads classification show that the introduction of quality values improves over standard alignment-free measures. These statistics are implemented in a software called QCluster (http://www.dei.unipd.it/~ciompin/main/qcluster.html).

VTBuilder: a tool for the assembly of multi isoform transcriptomes

Background

Within many research areas, such as transcriptomics, the millions of short DNA fragments (reads) produced by current sequencing platforms need to be assembled into transcript sequences before they can be utilized. Despite recent advances in assembly software, creating such transcripts from read data harboring isoform variation remains challenging. This is because current approaches fail to identify all variants present or they create chimeric transcripts within which relationships between co-evolving sites and other evolutionary factors are disrupted. We present VTBuilder, a tool for constructing non-chimeric transcripts from read data that has been sequenced from sources containing isoform complexity.

Results

We validated VTBuilder using reads simulated from 54 Sanger sequenced transcripts (SSTs) expressed in the venom gland of the saw scaled viper, Echis ocellatus. The SSTs were selected to represent genes from major co-expressed toxin groups known to harbor isoform variants. From the simulated reads, VTBuilder constructed 55 transcripts, 50 of which had a greater than 99% sequence similarity to 48 of the SSTs. In contrast, using the popular assembler tool Trinity (r2013-02-25), only 14 transcripts were constructed with a similar level of sequence identity to just 11 SSTs. Furthermore VTBuilder produced transcripts with a similar length distribution to the SSTs while those produced by Trinity were considerably shorter. To demonstrate that our approach can be scaled to real world data we assembled the venom gland transcriptome of the African puff adder Bitis arietans using paired-end reads sequenced on Illumina’s MiSeq platform. VTBuilder constructed 1481 transcripts from 5 million reads and, following annotation, all major toxin genes were recovered demonstrating reconstruction of complex underlying sequence and isoform diversity.

Conclusion

Unlike other approaches, VTBuilder strives to maintain the relationships between co-evolving sites within the constructed transcripts, and thus increases transcript utility for a wide range of research areas ranging from transcriptomics to phylogenetics and including the monitoring of drug resistant parasite populations. Additionally, improving the quality of transcripts assembled from read data will have an impact on future studies that query these data. VTBuilder has been implemented in java and is available, under the GPL GPU V0.3 license, from http:// http://www.lstmed.ac.uk/vtbuilder.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0389-8) contains supplementary material, which is available to authorized users.

Defining loci in restriction-based reduced representation genomic data from nonmodel species: sources of bias and diagnostics for optimal clustering

Next generation sequencing holds great promise for applications of phylogeography, landscape genetics, and population genomics in wild populations of nonmodel species, but the robustness of inferences hinges on careful experimental design and effective bioinformatic removal of predictable artifacts. Addressing this issue, we use published genomes from a tunicate, stickleback, and soybean to illustrate the potential for bioinformatic artifacts and introduce a protocol to minimize two sources of error expected from similarity-based de-novo clustering of stacked reads: the splitting of alleles into different clusters, which creates false homozygosity, and the grouping of paralogs into the same cluster, which creates false heterozygosity. We present an empirical application focused on Ciona savignyi, a tunicate with very high SNP heterozygosity (~0.05), because high diversity challenges the computational efficiency of most existing nonmodel pipelines while also potentially exacerbating paralog artifacts. The simulated and empirical data illustrate the advantages of using higher sequence difference clustering thresholds than is typical and demonstrate the utility of our protocol for efficiently identifying an optimum threshold from data without prior knowledge of heterozygosity. The empirical Ciona savignyi data also highlight null alleles as a potentially large source of false homozygosity in restriction-based reduced representation genomic data.

Development of an Arabis alpina genomic contig sequence data set and application to single nucleotide polymorphisms discovery

The alpine plant Arabis alpina is an emerging model in the ecological genomic field which is well suited to identifying the genes involved in local adaptation in contrasted environmental conditions, a subject which remains poorly understood at molecular level. This study presents the assembly of a pool of A. alpina genomic fragments using next-generation sequencing technologies. These contigs cover 172 Mb of the A. alpina genome (i.e. 50% of the genome) and were shown to contain sequences giving positive hits against 96% of the 458 CEGMA core genes (Core Eukaryotic Genes Mapping Approach), a set of highly conserved eukaryotic genes. Regions presenting high nucleic sequence identity with 77% of the close relative Arabidopsis thaliana's genes were found with an unbiased distribution across the different functional categories of A. thaliana genes. This new resource was tested using a resequencing assay to identify polymorphic sites. Sixteen samples were successfully analysed and 127,041 single-nucleotide polymorphisms identified. This contig data set will contribute to improving our understanding of the ecology of Arabis alpina, thus constituting an important resource for future ecological genomic studies.

Complete Arabis alpina chloroplast genome sequence and insight into its polymorphism

The alpine plant Arabis alpina (alpine rock-cress) is a thoroughly studied species in the fields of perennial plant flowering regulation, phylogeography, and adaptation to harsh alpine climatic conditions. We report the complete A. alpina chloroplast genome sequence obtained through de novo assembly of Illumina paired-end reads produced by total DNA sequencing. The A. alpina cp circular genome is 152,866 bp in length and built of two inverted repeats of 26,933 bp separated by unique regions: a large single copy of 82,338 bp and a small single copy of 17,938 bp. The genome contains 131 genes, some of them being duplicated in the inverted repeats. Seventy-nine unique protein-coding genes were annotated, together with 29 tRNA genes and 4 ribosomal RNA genes. Sequencing and mapping of 23 additional A. alpina DNA samples enabled to gain insight into the intraspecies polymorphism of the sequenced cp genome. Genetic variability among genomes was detected as 44 indels, most of them being located in noncoding regions, and 130 single-nucleotide polymorphisms, 37 of them corresponding to mutations in coding regions. A. alpina chloroplast genome sequence will be helpful in population studies or investigations of chloroplast functions of this alpine plant species.

•

We report the complete A. alpina chloroplast genome sequence through de novo assembly.

•

Arabis alpina plastome is 152,866 in length and harbors 131 genes.

•

Intraspecies polymorphism analysis was investigated among 24 samples.

•

We detected 44 indels and 130 single nucleotide polymorphisms.

An efficient and scalable graph modeling approach for capturing information at different levels in next generation sequencing reads

BACKGROUND

Next generation sequencing technologies have greatly advanced many research areas of the biomedical sciences through their capability to generate massive amounts of genetic information at unprecedented rates. The advent of next generation sequencing has led to the development of numerous computational tools to analyze and assemble the millions to billions of short sequencing reads produced by these technologies. While these tools filled an important gap, current approaches for storing, processing, and analyzing short read datasets generally have remained simple and lack the complexity needed to efficiently model the produced reads and assemble them correctly.

RESULTS

Previously, we presented an overlap graph coarsening scheme for modeling read overlap relationships on multiple levels. Most current read assembly and analysis approaches use a single graph or set of clusters to represent the relationships among a read dataset. Instead, we use a series of graphs to represent the reads and their overlap relationships across a spectrum of information granularity. At each information level our algorithm is capable of generating clusters of reads from the reduced graph, forming an integrated graph modeling and clustering approach for read analysis and assembly. Previously we applied our algorithm to simulated and real 454 datasets to assess its ability to efficiently model and cluster next generation sequencing data. In this paper we extend our algorithm to large simulated and real Illumina datasets to demonstrate that our algorithm is practical for both sequencing technologies.

CONCLUSIONS

Our overlap graph theoretic algorithm is able to model next generation sequencing reads at various levels of granularity through the process of graph coarsening. Additionally, our model allows for efficient representation of the read overlap relationships, is scalable for large datasets, and is practical for both Illumina and 454 sequencing technologies.

kClust: fast and sensitive clustering of large protein sequence databases

Background

Fueled by rapid progress in high-throughput sequencing, the size of public sequence databases doubles every two years. Searching the ever larger and more redundant databases is getting increasingly inefficient. Clustering can help to organize sequences into homologous and functionally similar groups and can improve the speed, sensitivity, and readability of homology searches. However, because the clustering time is quadratic in the number of sequences, standard sequence search methods are becoming impracticable.

Results

Here we present a method to cluster large protein sequence databases such as UniProt within days down to 20%–30% maximum pairwise sequence identity. kClust owes its speed and sensitivity to an alignment-free prefilter that calculates the cumulative score of all similar 6-mers between pairs of sequences, and to a dynamic programming algorithm that operates on pairs of similar 4-mers. To increase sensitivity further, kClust can run in profile-sequence comparison mode, with profiles computed from the clusters of a previous kClust iteration. kClust is two to three orders of magnitude faster than clustering based on NCBI BLAST, and on multidomain sequences of 20%–30% maximum pairwise sequence identity it achieves comparable sensitivity and a lower false discovery rate. It also compares favorably to CD-HIT and UCLUST in terms of false discovery rate, sensitivity, and speed.

Conclusions

kClust fills the need for a fast, sensitive, and accurate tool to cluster large protein sequence databases to below 30% sequence identity. kClust is freely available under GPL at http://toolkit.lmb.uni-muenchen.de/pub/kClust/.

Probabilistic error correction for RNA sequencing

Sequencing of RNAs (RNA-Seq) has revolutionized the field of transcriptomics, but the reads obtained often contain errors. Read error correction can have a large impact on our ability to accurately assemble transcripts. This is especially true for de novo transcriptome analysis, where a reference genome is not available. Current read error correction methods, developed for DNA sequence data, cannot handle the overlapping effects of non-uniform abundance, polymorphisms and alternative splicing. Here we present SEquencing Error CorrEction in Rna-seq data (SEECER), a hidden Markov Model (HMM)–based method, which is the first to successfully address these problems. SEECER efficiently learns hundreds of thousands of HMMs and uses these to correct sequencing errors. Using human RNA-Seq data, we show that SEECER greatly improves on previous methods in terms of quality of read alignment to the genome and assembly accuracy. To illustrate the usefulness of SEECER for de novo transcriptome studies, we generated new RNA-Seq data to study the development of the sea cucumber Parastichopus parvimensis. Our corrected assembled transcripts shed new light on two important stages in sea cucumber development. Comparison of the assembled transcripts to known transcripts in other species has also revealed novel transcripts that are unique to sea cucumber, some of which we have experimentally validated.

Supporting website: http://sb.cs.cmu.edu/seecer/.

A data parallel strategy for aligning multiple biological sequences on multi-core computers

In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.

In planta expression or delivery of potato aphid Macrosiphum euphorbiae effectors Me10 and Me23 enhances aphid fecundity

The interactions between aphids and their host plants seem to be analogous to those of plant-microbial pathogens. Unlike microbial pathogen effectors, little is known about aphid effectors and their ability to interfere with host immunity. To date, only three functional aphid effectors have been reported. To identify potato aphid (Macrosiphum euphorbiae) effectors, we developed a salivary gland transcriptome using Illumina technology. We generated 85 million Illumina reads from salivary glands and assembled them into 646 contigs. Ab initio sequence analysis predicted secretion signal peptides in 24% of these sequences, suggesting that they might be secreted into the plant during aphid feeding. Eight of these candidate effectors with secretion signal peptides were functionally characterized using Agrobacterium tumefaciens-mediated transient overexpression in Nicotiana benthamiana. Two candidate effectors, Me10 and Me23, increased aphid fecundity, suggesting their ability to suppress N. benthamiana defenses. Five of these candidate effectors, including Me10 and Me23, were also analyzed in tomato by delivering them through the Pseudomonas syringae type three secretion system. In tomato, only Me10 increased aphid fecundity. This work identified two additional aphid effectors with ability to manipulate the host for their advantage.

Computational analysis to predict functional role of hsa-miR-3065-3p as an antiviral therapeutic agent for treatment of triple infections: HCV, HIV-1, and HBV

BACKGROUND

Triple infection (TI) with HIV-1, HCV, and HBV (TI) is highly prevalent in intravenous drug users (IDUs). These TI patients have a faster progression to AIDS, and even after antiretroviral therapy (ART) the prognosis of their disease is poor. The use of microRNA (miRNA) to silence genes holds potential applications for anti-HCV therapy.

METHODS

We analyzed the role of human miRNAs (hsa-miRs) in TI by computational analyses for HCV, HIV-1, and HBV showing identity to these three viral genomes.

RESULTS

We identified one unique miRNA, hsa-miR-3065-3p, that shares significant mutual identity to these three viral genomes (∼61-83%). In addition, hsa-miR-99, hsa-miR-548, and hsa-miR-122 also showed mutual identity with these three viral genomes, albeit at a lower degree (∼52-88%).

CONCLUSION

Here, we present evidence using essential components of bioinformatics tools, and hypothesize that utility of hsa-miR-3065-3p and perhaps miR-548 would be potential antiviral therapeutic agents in the treatment of TI patients because it shows near perfect alignment in the seed region for all three viruses. We also make an argument that current proposed therapy with hsa-miR-122 may not be the optimal choice for HCV patients since it lacks essential gene alignment and may be harmful for the patients.

Ultrafast clustering algorithms for metagenomic sequence analysis

The rapid advances of high-throughput sequencing technologies dramatically prompted metagenomic studies of microbial communities that exist at various environments. Fundamental questions in metagenomics include the identities, composition and dynamics of microbial populations and their functions and interactions. However, the massive quantity and the comprehensive complexity of these sequence data pose tremendous challenges in data analysis. These challenges include but are not limited to ever-increasing computational demand, biased sequence sampling, sequence errors, sequence artifacts and novel sequences. Sequence clustering methods can directly answer many of the fundamental questions by grouping similar sequences into families. In addition, clustering analysis also addresses the challenges in metagenomics. Thus, a large redundant data set can be represented with a small non-redundant set, where each cluster can be represented by a single entry or a consensus. Artifacts can be rapidly detected through clustering. Errors can be identified, filtered or corrected by using consensus from sequences within clusters.

Sequence-based genotyping for marker discovery and co-dominant scoring in germplasm and populations

Conventional marker-based genotyping platforms are widely available, but not without their limitations. In this context, we developed Sequence-Based Genotyping (SBG), a technology for simultaneous marker discovery and co-dominant scoring, using next-generation sequencing. SBG offers users several advantages including a generic sample preparation method, a highly robust genome complexity reduction strategy to facilitate de novo marker discovery across entire genomes, and a uniform bioinformatics workflow strategy to achieve genotyping goals tailored to individual species, regardless of the availability of a reference sequence. The most distinguishing features of this technology are the ability to genotype any population structure, regardless whether parental data is included, and the ability to co-dominantly score SNP markers segregating in populations. To demonstrate the capabilities of SBG, we performed marker discovery and genotyping in Arabidopsis thaliana and lettuce, two plant species of diverse genetic complexity and backgrounds. Initially we obtained 1,409 SNPs for arabidopsis, and 5,583 SNPs for lettuce. Further filtering of the SNP dataset produced over 1,000 high quality SNP markers for each species. We obtained a genotyping rate of 201.2 genotypes/SNP and 58.3 genotypes/SNP for arabidopsis (n = 222 samples) and lettuce (n = 87 samples), respectively. Linkage mapping using these SNPs resulted in stable map configurations. We have therefore shown that the SBG approach presented provides users with the utmost flexibility in garnering high quality markers that can be directly used for genotyping and downstream applications. Until advances and costs will allow for routine whole-genome sequencing of populations, we expect that sequence-based genotyping technologies such as SBG will be essential for genotyping of model and non-model genomes alike.

Co-expression and co-responses: within and beyond transcription

Whole genome sequencing, the relative ease of transcript profiling by the use of microarrays and latterly RNA sequencing approaches have facilitated the capture of vast amounts of transcript data. However, despite the enormous progress made in gene annotation a substantial proportion of genes remain to be annotated at the functional level. Considerable progress has, however, been made by searching for transcriptional coordination between genes of known function and non-annotated genes on the premise that such co-expressed genes tend to be functionally related. Here we review progress made following this approach as well as its expansion to include phenotypic information from other levels of cellular organization such as proteomic and metabolomic data as well as physiological and developmental phenotypes.