Types Of Sequence Data Research Articles

Special collection of recent advances in next‐generation bioinformatics, part II

Recent advances in quantitative biology has attested the important roles bioinformatics play in biological sciences as well as the great expansion of the scope of next-generation bioinformatics to all areas of systems biology. From the last issue of Quantitative Biology, we started to publish a special collection of reviews and research articles on next-generation bioinformatics to reflect this trend. The collection is dedicated to the completion of the 5-year center grant (#2012CB316500) on next-generation bioinformatics under the National Basic Research Program of China or the “973 Program”. The project aims to advance research on basic bioinformatics methodologies for processing next-generation biological data especially multiple types of sequencing data, and for converting the information buried in the data into quantitative understanding of important biological processes. The papers in the special collection were selected from submissions by the principle investigators and their collaborators of this project, after the standard peer-review procedure of Quantitative Biology. They reflect current progresses from the team and the whole community in several major directions that the project covers. The special collection will be continued in this issue and the next issue. Four papers are published in this issue. In the last issue, Zhao et al. reviewed methods for processing RNA-sequencing data for transcriptomes for which the reference genome is available. In this issue, Li et al. from the subproject 1 told the other aspect of the story by reviewing methods for processing RNA-sequencing data when the reference genome is not available or complete. The title of the paper is “De novo assembly of transcriptome from next-generation sequencing data”. In genomic sequencing analysis for genetics studies, obtaining haplotype data is an important and difficult task. In the paper “Comparison of the experimental methods in haplotype sequencing via next generation sequencing”, Tu et al. from subproject 1 reviewed major technologies for haplotype sequencing, and compared their performances and characteristics. Wu et al. from subproject 4 reviewedmajor sequencing platforms and bioinformatics strategies and methods for medical genetics studies based on nextgeneration sequencing, with the title “Whole genome sequencing and its applications in medical genetics”. It provided a rich source for bioinformatics tools available in this field and discussed existing open issues and possible future directions for dealing with the issues. Genomes are organized in three-dimensional space rather than one-dimensional linear space. Although analyzing the 1D genome sequence is fundamental in genetics studies, obtaining the 3D structure is crucial for understanding many key aspects of the genome organization and its relation to higher order gene regulation. In the paper “Developing bioimaging and quantitative methods to study 3D genome”, Gao et al. from subproject 3 reviewed existing knowledge on the structural organization of genomes, and the advances in super-resolution microscopy techniques for directly detecting 3D structure of a genome. Progresses on software for processing and visualizing Hi-C sequencing data are also introduced, followed by discussions on future trends for integrating image-based approaches and sequencing-based approaches for better understanding the 3D genome organization. Another four papers of the special collection will be published in the next issue. In the paper “An overview of

Empirical estimation of sequencing error rates using smoothing splines.

BackgroundNext-generation sequencing has been used by investigators to address a diverse range of biological problems through, for example, polymorphism and mutation discovery and microRNA profiling. However, compared to conventional sequencing, the error rates for next-generation sequencing are often higher, which impacts the downstream genomic analysis. Recently, Wang et al. (BMC Bioinformatics 13:185, 2012) proposed a shadow regression approach to estimate the error rates for next-generation sequencing data based on the assumption of a linear relationship between the number of reads sequenced and the number of reads containing errors (denoted as shadows). However, this linear read-shadow relationship may not be appropriate for all types of sequence data. Therefore, it is necessary to estimate the error rates in a more reliable way without assuming linearity. We proposed an empirical error rate estimation approach that employs cubic and robust smoothing splines to model the relationship between the number of reads sequenced and the number of shadows.ResultsWe performed simulation studies using a frequency-based approach to generate the read and shadow counts directly, which can mimic the real sequence counts data structure. Using simulation, we investigated the performance of the proposed approach and compared it to that of shadow linear regression. The proposed approach provided more accurate error rate estimations than the shadow linear regression approach for all the scenarios tested. We also applied the proposed approach to assess the error rates for the sequence data from the MicroArray Quality Control project, a mutation screening study, the Encyclopedia of DNA Elements project, and bacteriophage PhiX DNA samples.ConclusionsThe proposed empirical error rate estimation approach does not assume a linear relationship between the error-free read and shadow counts and provides more accurate estimations of error rates for next-generation, short-read sequencing data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1052-3) contains supplementary material, which is available to authorized users.

The Diversity and Molecular Evolution of B-Cell Receptors during Infection

B-cell receptors (BCRs) are membrane-bound immunoglobulins that recognize and bind foreign proteins (antigens). BCRs are formed through random somatic changes of germline DNA, creating a vast repertoire of unique sequences that enable individuals to recognize a diverse range of antigens. After encountering antigen for the first time, BCRs undergo a process of affinity maturation, whereby cycles of rapid somatic mutation and selection lead to improved antigen binding. This constitutes an accelerated evolutionary process that takes place over days or weeks. Next-generation sequencing of the gene regions that determine BCR binding has begun to reveal the diversity and dynamics of BCR repertoires in unprecedented detail. Although this new type of sequence data has the potential to revolutionize our understanding of infection dynamics, quantitative analysis is complicated by the unique biology and high diversity of BCR sequences. Models and concepts from molecular evolution and phylogenetics that have been applied successfully to rapidly evolving pathogen populations are increasingly being adopted to study BCR diversity and divergence within individuals. However, BCR dynamics may violate key assumptions of many standard evolutionary methods, as they do not descend from a single ancestor, and experience biased mutation. Here, we review the application of evolutionary models to BCR repertoires and discuss the issues we believe need be addressed for this interdisciplinary field to flourish.

MG-RAST, a Metagenomics Service for Analysis of Microbial Community Structure and Function.

Approaches in molecular biology, particularly those that deal with high-throughput sequencing of entire microbial communities (the field of metagenomics), are rapidly advancing our understanding of the composition and functional content of microbial communities involved in climate change, environmental pollution, human health, biotechnology, etc. Metagenomics provides researchers with the most complete picture of the taxonomic (i.e., what organisms are there) and functional (i.e., what are those organisms doing) composition of natively sampled microbial communities, making it possible to perform investigations that include organisms that were previously intractable to laboratory-controlled culturing; currently, these constitute the vast majority of all microbes on the planet. All organisms contained in environmental samples are sequenced in a culture-independent manner, most often with 16S ribosomal amplicon methods to investigate the taxonomic or whole-genome shotgun-based methods to investigate the functional content of sampled communities. Metagenomics allows researchers to characterize the community composition and functional content of microbial communities, but it cannot show which functional processes are active; however, near parallel developments in transcriptomics promise a dramatic increase in our knowledge in this area as well. Since 2008, MG-RAST (Meyer et al., BMC Bioinformatics 9:386, 2008) has served as a public resource for annotation and analysis of metagenomic sequence data, providing a repository that currently houses more than 150,000 data sets (containing 60+ tera-base-pairs) with more than 23,000 publically available. MG-RAST, or the metagenomics RAST (rapid annotation using subsystems technology) server makes it possible for users to upload raw metagenomic sequence data in (preferably) fastq or fasta format. Assessments of sequence quality, annotation with respect to multiple reference databases, are performed automatically with minimal input from the user (see Subheading 4 at the end of this chapter for more details). Post-annotation analysis and visualization are also possible, directly through the web interface, or with tools like matR (metagenomic analysis tools for R, covered later in this chapter) that utilize the MG-RAST API ( http://api.metagenomics.anl.gov/api.html ) to easily download data from any stage in the MG-RAST processing pipeline. Over the years, MG-RAST has undergone substantial revisions to keep pace with the dramatic growth in the number, size, and types of sequence data that accompany constantly evolving developments in metagenomics and related -omic sciences (e.g., metatranscriptomics).

Nuclear RNA Isolation and Sequencing.

Most transcriptome studies involve sequencing and quantification of steady-state mRNA by isolating and sequencing poly (A) RNA. Although this type of sequencing data is informative to determine steady-state mRNA levels it does not provide information on transcriptional output and thus may not always reflect changes in transcriptional regulation of gene expression. Furthermore, sequencing poly (A) RNA may miss transcribed regions of the genome not usually modified by polyadenylation which includes many long noncoding RNAs. Here, we describe nuclear-RNA sequencing (nucRNA-seq) which investigates the transcriptional landscape through sequencing and quantification of nuclear RNAs which are both unspliced and spliced transcripts for protein-coding genes and nuclear-retained long noncoding RNAs.

Csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.

Chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) is widely used to identify binding sites for a target protein in the genome. An important scientific application is to identify changes in protein binding between different treatment conditions, i.e. to detect differential binding. This can reveal potential mechanisms through which changes in binding may contribute to the treatment effect. The csaw package provides a framework for the de novo detection of differentially bound genomic regions. It uses a window-based strategy to summarize read counts across the genome. It exploits existing statistical software to test for significant differences in each window. Finally, it clusters windows into regions for output and controls the false discovery rate properly over all detected regions. The csaw package can handle arbitrarily complex experimental designs involving biological replicates. It can be applied to both transcription factor and histone mark datasets, and, more generally, to any type of sequencing data measuring genomic coverage. csaw performs favorably against existing methods for de novo DB analyses on both simulated and real data. csaw is implemented as a R software package and is freely available from the open-source Bioconductor project.

P53 transcriptional programs in B cells upon exposure to genotoxic stress in vivo: Computational analysis of next-generation sequencing data.

The transcriptional programs activated by p53 in B cells in vivo following exposure to ionizing radiation were studied through the integrated analysis of various types of next-generation sequencing data: genome-wide profiling of p53 binding sites, mapping of histone marks and open chromatin regions and quantification of gene expression. Moreover, the binding of p53 was associated to a series of specific motifs on the DNA, which were directly inferred from the data. Here, we describe in detail the computational analysis of the datasets associated with our study (Tonelli et al., Oncotarget 6 (2015), 24611-26), deposited in the GEO archive (accession code GSE71180), and we provide the R scripts needed to generated the figures of the paper.

GroHMM: a computational tool for identifying unannotated and cell type-specific transcription units from global run-on sequencing data.

BackgroundGlobal run-on coupled with deep sequencing (GRO-seq) provides extensive information on the location and function of coding and non-coding transcripts, including primary microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and enhancer RNAs (eRNAs), as well as yet undiscovered classes of transcripts. However, few computational tools tailored toward this new type of sequencing data are available, limiting the applicability of GRO-seq data for identifying novel transcription units.ResultsHere, we present groHMM, a computational tool in R, which defines the boundaries of transcription units de novo using a two state hidden-Markov model (HMM). A systematic comparison of the performance between groHMM and two existing peak-calling methods tuned to identify broad regions (SICER and HOMER) favorably supports our approach on existing GRO-seq data from MCF-7 breast cancer cells. To demonstrate the broader utility of our approach, we have used groHMM to annotate a diverse array of transcription units (i.e., primary transcripts) from four GRO-seq data sets derived from cells representing a variety of different human tissue types, including non-transformed cells (cardiomyocytes and lung fibroblasts) and transformed cells (LNCaP and MCF-7 cancer cells), as well as non-mammalian cells (from flies and worms). As an example of the utility of groHMM and its application to questions about the transcriptome, we show how groHMM can be used to analyze cell type-specific enhancers as defined by newly annotated enhancer transcripts.ConclusionsOur results show that groHMM can reveal new insights into cell type-specific transcription by identifying novel transcription units, and serve as a complete and useful tool for evaluating functional genomic elements in cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0656-3) contains supplementary material, which is available to authorized users.

VirSorter: mining viral signal from microbial genomic data.

Viruses of microbes impact all ecosystems where microbes drive key energy and substrate transformations including the oceans, humans and industrial fermenters. However, despite this recognized importance, our understanding of viral diversity and impacts remains limited by too few model systems and reference genomes. One way to fill these gaps in our knowledge of viral diversity is through the detection of viral signal in microbial genomic data. While multiple approaches have been developed and applied for the detection of prophages (viral genomes integrated in a microbial genome), new types of microbial genomic data are emerging that are more fragmented and larger scale, such as Single-cell Amplified Genomes (SAGs) of uncultivated organisms or genomic fragments assembled from metagenomic sequencing. Here, we present VirSorter, a tool designed to detect viral signal in these different types of microbial sequence data in both a reference-dependent and reference-independent manner, leveraging probabilistic models and extensive virome data to maximize detection of novel viruses. Performance testing shows that VirSorter’s prophage prediction capability compares to that of available prophage predictors for complete genomes, but is superior in predicting viral sequences outside of a host genome (i.e., from extrachromosomal prophages, lytic infections, or partially assembled prophages). Furthermore, VirSorter outperforms existing tools for fragmented genomic and metagenomic datasets, and can identify viral signal in assembled sequence (contigs) as short as 3kb, while providing near-perfect identification (>95% Recall and 100% Precision) on contigs of at least 10kb. Because VirSorter scales to large datasets, it can also be used in “reverse” to more confidently identify viral sequence in viral metagenomes by sorting away cellular DNA whether derived from gene transfer agents, generalized transduction or contamination. Finally, VirSorter is made available through the iPlant Cyberinfrastructure that provides a web-based user interface interconnected with the required computing resources. VirSorter thus complements existing prophage prediction softwares to better leverage fragmented, SAG and metagenomic datasets in a way that will scale to modern sequencing. Given these features, VirSorter should enable the discovery of new viruses in microbial datasets, and further our understanding of uncultivated viral communities across diverse ecosystems.

EnD-Seq and AppEnD: sequencing 3' ends to identify nontemplated tails and degradation intermediates.

Existing methods for detecting RNA intermediates resulting from exonuclease degradation are low-throughput and laborious. In addition, mapping the 3′ ends of RNA molecules to the genome after high-throughput sequencing is challenging, particularly if the 3′ ends contain post-transcriptional modifications. To address these problems, we developed EnD-Seq, a high-throughput sequencing protocol that preserves the 3′ end of RNA molecules, and AppEnD, a computational method for analyzing high-throughput sequencing data. Together these allow determination of the 3′ ends of RNA molecules, including nontemplated additions. Applying EnD-Seq and AppEnD to histone mRNAs revealed that a significant fraction of cytoplasmic histone mRNAs end in one or two uridines, which have replaced the 1–2 nt at the 3′ end of mature histone mRNA maintaining the length of the histone transcripts. Histone mRNAs in fly embryos and ovaries show the same pattern, but with different tail nucleotide compositions. We increase the sensitivity of EnD-Seq by using cDNA priming to specifically enrich low-abundance tails of known sequence composition allowing identification of degradation intermediates. In addition, we show the broad applicability of our computational approach by using AppEnD to gain insight into 3′ additions from diverse types of sequencing data, including data from small capped RNA sequencing and some alternative polyadenylation protocols.

Combining genomic sequencing methods to explore viral diversity and reveal potential virus-host interactions.

Viral diversity and virus-host interactions in oxygen-starved regions of the ocean, also known as oxygen minimum zones (OMZs), remain relatively unexplored. Microbial community metabolism in OMZs alters nutrient and energy flow through marine food webs, resulting in biological nitrogen loss and greenhouse gas production. Thus, viruses infecting OMZ microbes have the potential to modulate community metabolism with resulting feedback on ecosystem function. Here, we describe viral communities inhabiting oxic surface (10 m) and oxygen-starved basin (200 m) waters of Saanich Inlet, a seasonally anoxic fjord on the coast of Vancouver Island, British Columbia using viral metagenomics and complete viral fosmid sequencing on samples collected between April 2007 and April 2010. Of 6459 open reading frames (ORFs) predicted across all 34 viral fosmids, 77.6% (n = 5010) had no homology to reference viral genomes. These fosmids recruited a higher proportion of viral metagenomic sequences from Saanich Inlet than from nearby northeastern subarctic Pacific Ocean (Line P) waters, indicating differences in the viral communities between coastal and open ocean locations. While functional annotations of fosmid ORFs were limited, recruitment to NCBI's non-redundant “nr” database and publicly available single-cell genomes identified putative viruses infecting marine thaumarchaeal and SUP05 proteobacteria to provide potential host linkages with relevance to coupled biogeochemical cycling processes in OMZ waters. Taken together, these results highlight the power of coupled analyses of multiple sequence data types, such as viral metagenomic and fosmid sequence data with prokaryotic single cell genomes, to chart viral diversity, elucidate genomic and ecological contexts for previously unclassifiable viral sequences, and identify novel host interactions in natural and engineered ecosystems.

Towards the unification of sequence-based classification and sequence-based identification of host-associated microorganisms.

Towards the unification of sequence-based classification and sequence-based identification of host-associated microorganisms.

Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement.

Advances in modern sequencing technologies allow us to generate sufficient data to analyze hundreds of bacterial genomes from a single machine in a single day. This potential for sequencing massive numbers of genomes calls for fully automated methods to produce high-quality assemblies and variant calls. We introduce Pilon, a fully automated, all-in-one tool for correcting draft assemblies and calling sequence variants of multiple sizes, including very large insertions and deletions. Pilon works with many types of sequence data, but is particularly strong when supplied with paired end data from two Illumina libraries with small e.g., 180 bp and large e.g., 3–5 Kb inserts. Pilon significantly improves draft genome assemblies by correcting bases, fixing mis-assemblies and filling gaps. For both haploid and diploid genomes, Pilon produces more contiguous genomes with fewer errors, enabling identification of more biologically relevant genes. Furthermore, Pilon identifies small variants with high accuracy as compared to state-of-the-art tools and is unique in its ability to accurately identify large sequence variants including duplications and resolve large insertions. Pilon is being used to improve the assemblies of thousands of new genomes and to identify variants from thousands of clinically relevant bacterial strains. Pilon is freely available as open source software.

Metabolic network prediction through pairwise rational kernels.

BackgroundMetabolic networks are represented by the set of metabolic pathways. Metabolic pathways are a series of biochemical reactions, in which the product (output) from one reaction serves as the substrate (input) to another reaction. Many pathways remain incompletely characterized. One of the major challenges of computational biology is to obtain better models of metabolic pathways. Existing models are dependent on the annotation of the genes. This propagates error accumulation when the pathways are predicted by incorrectly annotated genes. Pairwise classification methods are supervised learning methods used to classify new pair of entities. Some of these classification methods, e.g., Pairwise Support Vector Machines (SVMs), use pairwise kernels. Pairwise kernels describe similarity measures between two pairs of entities. Using pairwise kernels to handle sequence data requires long processing times and large storage. Rational kernels are kernels based on weighted finite-state transducers that represent similarity measures between sequences or automata. They have been effectively used in problems that handle large amount of sequence information such as protein essentiality, natural language processing and machine translations.ResultsWe create a new family of pairwise kernels using weighted finite-state transducers (called Pairwise Rational Kernel (PRK)) to predict metabolic pathways from a variety of biological data. PRKs take advantage of the simpler representations and faster algorithms of transducers. Because raw sequence data can be used, the predictor model avoids the errors introduced by incorrect gene annotations. We then developed several experiments with PRKs and Pairwise SVM to validate our methods using the metabolic network of Saccharomyces cerevisiae. As a result, when PRKs are used, our method executes faster in comparison with other pairwise kernels. Also, when we use PRKs combined with other simple kernels that include evolutionary information, the accuracy values have been improved, while maintaining lower construction and execution times.ConclusionsThe power of using kernels is that almost any sort of data can be represented using kernels. Therefore, completely disparate types of data can be combined to add power to kernel-based machine learning methods. When we compared our proposal using PRKs with other similar kernel, the execution times were decreased, with no compromise of accuracy. We also proved that by combining PRKs with other kernels that include evolutionary information, the accuracy can also also be improved. As our proposal can use any type of sequence data, genes do not need to be properly annotated, avoiding accumulation errors because of incorrect previous annotations.

Evaluation and validation of de novo and hybrid assembly techniques to derive high-quality genome sequences.

Motivation: To assess the potential of different types of sequence data combined with de novo and hybrid assembly approaches to improve existing draft genome sequences.Results: Illumina, 454 and PacBio sequencing technologies were used to generate de novo and hybrid genome assemblies for four different bacteria, which were assessed for quality using summary statistics (e.g. number of contigs, N50) and in silico evaluation tools. Differences in predictions of multiple copies of rDNA operons for each respective bacterium were evaluated by PCR and Sanger sequencing, and then the validated results were applied as an additional criterion to rank assemblies. In general, assemblies using longer PacBio reads were better able to resolve repetitive regions. In this study, the combination of Illumina and PacBio sequence data assembled through the ALLPATHS-LG algorithm gave the best summary statistics and most accurate rDNA operon number predictions. This study will aid others looking to improve existing draft genome assemblies.Availability and implementation: All assembly tools except CLC Genomics Workbench are freely available under GNU General Public License.Contact: brownsd@ornl.govSupplementary information: Supplementary data are available at Bioinformatics online.

Bayesian clustering of DNA sequences using Markov chains and a stochastic partition model

In many biological applications it is necessary to cluster DNA sequences into groups that represent underlying organismal units, such as named species or genera. In metagenomics this grouping needs typically to be achieved on the basis of relatively short sequences which contain different types of errors, making the use of a statistical modeling approach desirable. Here we introduce a novel method for this purpose by developing a stochastic partition model that clusters Markov chains of a given order. The model is based on a Dirichlet process prior and we use conjugate priors for the Markov chain parameters which enables an analytical expression for comparing the marginal likelihoods of any two partitions. To find a good candidate for the posterior mode in the partition space, we use a hybrid computational approach which combines the EM-algorithm with a greedy search. This is demonstrated to be faster and yield highly accurate results compared to earlier suggested clustering methods for the metagenomics application. Our model is fairly generic and could also be used for clustering of other types of sequence data for which Markov chains provide a reasonable way to compress information, as illustrated by experiments on shotgun sequence type data from an Escherichia coli strain.

Constructing Hepitypes: Phasing Local Genotype and DNA Methylation

Whole-genome DNA methylation sequencing provides both methylation patterns and genetic information. We utilized base resolution methylomes to directly identify allelic linkage of DNA methylation and genomic variants. The paired association was further extended to construct hepitypes by the simultaneous phasing of genotype and methylation. Using such approach, the sequencing reads provide direct statistics of the interdependence between methylcytosines and nucleotide variations; consequently, the detailed patterns of genetic and epigenetic variations can be readily inferred by data. Moreover, the analysis is not limited by known single nucleotide variants. We demonstrate the utility of our method by identifying methylation sites that were strongly associated with genetic variations in the human genome using H1 and IMR90 methylomes. In addition to imprinted regions and SNV-in-CpG sites, we show numerous cis-regulatory sequence-associated DNA methylation sites. We next extended this strategy to incorporate multiple nucleotide and methylation sites and ranked hepitypes according to the observed frequency. The top-ranked hepitypes indicate that methylated sites are often observed from the same allele. Moreover, we used the informative nucleotide variants in both methylation and expression data of the same cell lines to investigate the extent of allele-specific gene regulation. We identified a set of genes for which the exonic allele-specific methylation patterns are correlated with their allele-specific expression levels. Allele-specific methylation plays an essential role in allelic regulation. Our finding may shed light on the function of non-coding nucleotide variations, DNA methylation polymorphism and inter-individual differences. This approach is applicable to any largescale differential methylation studies and can integrate various types of high-throughput sequencing data.

PolyCat: A Resource for Genome Categorization of Sequencing Reads From Allopolyploid Organisms

Read mapping is a fundamental part of next-generation genomic research but is complicated by genome duplication in many plants. Categorizing DNA sequence reads into their respective genomes enables current methods to analyze polyploid genomes as if they were diploid. We present PolyCat—a pipeline for mapping and categorizing all types of next-generation sequence data produced from allopolyploid organisms. PolyCat uses GSNAP’s single-nucleotide polymorphism (SNP)-tolerant mapping to minimize the mapping efficiency bias caused by SNPs between genomes. PolyCat then uses SNPs between genomes to categorize reads according to their respective genomes. Bisulfite-treated reads have a significant reduction in nucleotide complexity because nucleotide conversion events are confounded with transition substitutions. PolyCat includes special provisions to properly handle bisulfite-treated data. We demonstrate the functionality of PolyCat on allotetraploid cotton, Gossypium hirsutum, and create a functional SNP index for efficiently mapping sequence reads to the D-genome sequence of G. raimondii. PolyCat is appropriate for all allopolyploids and all types of next-generation genome analysis, including differential expression (RNA sequencing), differential methylation (bisulfite sequencing), differential DNA-protein binding (chromatin immunoprecipitation sequencing), and population diversity.

Erratum to: CoNVEX: copy number variation estimation in exome sequencing data using HMM

Our proposed method, to detect copy number variations in whole exome sequencing data [1] was published in BMC Bioinformatics as a special issue containing the proceedings of The Eleventh Asia Pacific Bioinformatics Conference (APBC 2013). After the publication, it was brought to our attention that name of our software has a conflict with another software developed by a project carried out at Wellcome Trust Sanger Institute (http://www.uk10k.org/assets/ashg_vijayarangakannan_etal_2012.pdf). We were not aware of this project when we first named our method as CoNVEX, in mid 2012. Therefore, we would like to thank Dr. P. Vijayarangakannan, one of the developers of the other method, for bringing this to our attention. We would like to mention that, although both software share the same name, they are different in terms of computational methods and types of exome sequencing data used. To avoid any confusion associated with the software name, we would no longer call our software as CoNVEX. Our software will be further developed with a new project name, Aberration Detection in Tumour Exome (ADTEx).

Data compression for sequencing data

Post-Sanger sequencing methods produce tons of data, and there is a general agreement that the challenge to store and process them must be addressed with data compression. In this review we first answer the question “why compression” in a quantitative manner. Then we also answer the questions “what” and “how”, by sketching the fundamental compression ideas, describing the main sequencing data types and formats, and comparing the specialized compression algorithms and tools. Finally, we go back to the question “why compression” and give other, perhaps surprising answers, demonstrating the pervasiveness of data compression techniques in computational biology.