Mobster: accurate detection of mobile element insertions in next generation sequencing data

Mobile elements are major drivers in changing genomic architecture and can cause disease. The detection of mobile elements is hindered due to the low mappability of their highly repetitive sequences. We have developed an algorithm, called Mobster, to detect non-reference mobile element insertions in next generation sequencing data from both whole genome and whole exome studies. Mobster uses discordant read pairs and clipped reads in combination with consensus sequences of known active mobile elements. Mobster has a low false discovery rate and high recall rate for both L1 and Alu elements. Mobster is available at http://sourceforge.net/projects/mobster.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0488-x) contains supplementary material, which is available to authorized users.     

PHYSICO2: an UNIX based standalone procedure for computation of physicochemical,window-dependent and substitution based evolutionary properties of protein sequences along with automated block preparation tool,version 2

AvailabilityPHYSICO2: is freely available at http://sourceforge.net/projects/physico2/ along with its documentation at https://sourceforge.net/projects/physico2/files/Documentation.pdf/download for all users.     

Compression of FASTQ and SAM Format Sequencing Data

Storage and transmission of the data produced by modern DNA sequencing instruments has become a major concern, which prompted the Pistoia Alliance to pose the SequenceSqueeze contest for compression of FASTQ files. We present several compression entries from the competition, Fastqz and Samcomp/Fqzcomp, including the winning entry. These are compared against existing algorithms for both reference based compression (CRAM, Goby) and non-reference based compression (DSRC, BAM) and other recently published competition entries (Quip, SCALCE). The tools are shown to be the new Pareto frontier for FASTQ compression, offering state of the art ratios at affordable CPU costs. All programs are freely available on SourceForge. Fastqz: https://sourceforge.net/projects/fastqz/, fqzcomp: https://sourceforge.net/projects/fqzcomp/, and samcomp: https://sourceforge.net/projects/samcomp/.     

ADSBET2: Automated Determination of Salt-Bridge Energy-Terms version 2

AvailabilityADSBET2 is freely available at http://sourceforge.net/projects/ADSBET2/ for all users.     

SBION2: Analyses of Salt Bridges from Multiple Structure Files,Version 2

AvailabilitySBION2 is freely available at http://sourceforge.net/projects/sbion2/ for academic users     

ABC Transporters and the Proteasome Complex Are Implicated in Susceptibility to Stevens–Johnson Syndrome and Toxic Epidermal Necrolysis across Multiple Drugs

Stevens–Johnson syndrome (SJS) and Toxic Epidermal Necrolysis (TEN) represent rare but serious adverse drug reactions (ADRs). Both are characterized by distinctive blistering lesions and significant mortality rates. While there is evidence for strong drug-specific genetic predisposition related to HLA alleles, recent genome wide association studies (GWAS) on European and Asian populations have failed to identify genetic susceptibility alleles that are common across multiple drugs. We hypothesize that this is a consequence of the low to moderate effect size of individual genetic risk factors. To test this hypothesis we developed Pointer, a new algorithm that assesses the aggregate effect of multiple low risk variants on a pathway using a gene set enrichment approach. A key advantage of our method is the capability to associate SNPs with genes by exploiting physical proximity as well as by using expression quantitative trait loci (eQTLs) that capture information about both cis- and trans-acting regulatory effects. We control for known bias-inducing aspects of enrichment based analyses, such as: 1) gene length, 2) gene set size, 3) presence of biologically related genes within the same linkage disequilibrium (LD) region, and, 4) genes shared among multiple gene sets. We applied this approach to publicly available SJS/TEN genome-wide genotype data and identified the ABC transporter and Proteasome pathways as potentially implicated in the genetic susceptibility of non-drug-specific SJS/TEN. We demonstrated that the innovative SNP-to-gene mapping phase of the method was essential in detecting the significant enrichment for those pathways. Analysis of an independent gene expression dataset provides supportive functional evidence for the involvement of Proteasome pathways in SJS/TEN cutaneous lesions. These results suggest that Pointer provides a useful framework for the integrative analysis of pharmacogenetic GWAS data, by increasing the power to detect aggregate effects of multiple low risk variants. The software is available for download at https://sourceforge.net/projects/pointergsa/.     

EXCAVATOR: detecting copy number variants from whole-exome sequencing data

We developed a novel software tool, EXCAVATOR, for the detection of copy number variants (CNVs) from whole-exome sequencing data. EXCAVATOR combines a three-step normalization procedure with a novel heterogeneous hidden Markov model algorithm and a calling method that classifies genomic regions into five copy number states. We validate EXCAVATOR on three datasets and compare the results with three other methods. These analyses show that EXCAVATOR outperforms the other methods and is therefore a valuable tool for the investigation of CNVs in largescale projects, as well as in clinical research and diagnostics. EXCAVATOR is freely available at http://sourceforge.net/projects/excavatortool/.     

Combinatorial Conflicting Homozygosity (CCH) analysis enables the rapid identification of shared genomic regions in the presence of multiple phenocopies

Background

The ability to identify regions of the genome inherited with a dominant trait in one or more families has become increasingly valuable with the wide availability of high throughput sequencing technology. While a number of methods exist for mapping of homozygous variants segregating with recessive traits in consanguineous families, dominant conditions are conventionally analysed by linkage analysis, which requires computationally demanding haplotype reconstruction from marker genotypes and, even using advanced parallel approximation implementations, can take substantial time, particularly for large pedigrees. In addition, linkage analysis lacks sensitivity in the presence of phenocopies (individuals sharing the trait but not the genetic variant responsible). Combinatorial Conflicting Homozygosity (CCH) analysis uses high density biallelic single nucleotide polymorphism (SNP) marker genotypes to identify genetic loci within which consecutive markers are not homozygous for different alleles. This allows inference of identical by descent (IBD) inheritance of a haplotype among a set or subsets of related or unrelated individuals.

Results

A single genome-wide conflicting homozygosity analysis takes <3 seconds and parallelisation permits multiple combinations of subsets of individuals to be analysed quickly. Analysis of unrelated individuals demonstrated that in the absence of IBD inheritance, runs of no CH exceeding 4 cM are not observed. At this threshold, CCH is >97% sensitive and specific for IBD regions within a pedigree exceeding this length and was able to identify the locus responsible for a dominantly inherited kidney disease in a Turkish Cypriot family in which six out 17 affected individuals were phenocopies. It also revealed shared ancestry at the disease-linked locus among affected individuals from two different Cypriot populations.

Conclusions

CCH does not require computationally demanding haplotype reconstruction and can detect regions of shared inheritance of a haplotype among subsets of related or unrelated individuals directly from SNP genotype data. In contrast to parametric linkage allowing for phenocopies, CCH directly provides the exact number and identity of individuals sharing each locus. CCH can also identify regions of shared ancestry among ostensibly unrelated individuals who share a trait. CCH is implemented in Python and is freely available (as source code) from http://sourceforge.net/projects/cchsnp/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1360-4) contains supplementary material, which is available to authorized users.     

miRPlant: an integrated tool for identification of plant miRNA from RNA sequencing data

Background

Small RNA sequencing is commonly used to identify novel miRNAs and to determine their expression levels in plants. There are several miRNA identification tools for animals such as miRDeep, miRDeep2 and miRDeep*. miRDeep-P was developed to identify plant miRNA using miRDeep’s probabilistic model of miRNA biogenesis, but it depends on several third party tools and lacks a user-friendly interface. The objective of our miRPlant program is to predict novel plant miRNA, while providing a user-friendly interface with improved accuracy of prediction.

Result

We have developed a user-friendly plant miRNA prediction tool called miRPlant. We show using 16 plant miRNA datasets from four different plant species that miRPlant has at least a 10% improvement in accuracy compared to miRDeep-P, which is the most popular plant miRNA prediction tool. Furthermore, miRPlant uses a Graphical User Interface for data input and output, and identified miRNA are shown with all RNAseq reads in a hairpin diagram.

Conclusions

We have developed miRPlant which extends miRDeep* to various plant species by adopting suitable strategies to identify hairpin excision regions and hairpin structure filtering for plants. miRPlant does not require any third party tools such as mapping or RNA secondary structure prediction tools. miRPlant is also the first plant miRNA prediction tool that dynamically plots miRNA hairpin structure with small reads for identified novel miRNAs. This feature will enable biologists to visualize novel pre-miRNA structure and the location of small RNA reads relative to the hairpin. Moreover, miRPlant can be easily used by biologists with limited bioinformatics skills.miRPlant and its manual are freely available at http://www.australianprostatecentre.org/research/software/mirplant or http://sourceforge.net/projects/mirplant/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-275) contains supplementary material, which is available to authorized users.     

SEWAL: an open-source platform for next-generation sequence analysis and visualization

Next-generation DNA sequencing platforms provide exciting new possibilities for in vitro genetic analysis of functional nucleic acids. However, the size of the resulting data sets presents computational and analytical challenges. We present an open-source software package that employs a locality-sensitive hashing algorithm to enumerate all unique sequences in an entire Illumina sequencing run (∼10⁸ sequences). The algorithm results in quasilinear time processing of entire Illumina lanes (∼10⁷ sequences) on a desktop computer in minutes. To facilitate visual analysis of sequencing data, the software produces three-dimensional scatter plots similar in concept to Sewall Wright and John Maynard Smith’s adaptive or fitness landscape. The software also contains functions that are particularly useful for doped selections such as mutation frequency analysis, information content calculation, multivariate statistical functions (including principal component analysis), sequence distance metrics, sequence searches and sequence comparisons across multiple Illumina data sets. Source code, executable files and links to sample data sets are available at http://www.sourceforge.net/projects/sewal.     

LoFreq: a sequence-quality aware,ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets

The study of cell-population heterogeneity in a range of biological systems, from viruses to bacterial isolates to tumor samples, has been transformed by recent advances in sequencing throughput. While the high-coverage afforded can be used, in principle, to identify very rare variants in a population, existing ad hoc approaches frequently fail to distinguish true variants from sequencing errors. We report a method (LoFreq) that models sequencing run-specific error rates to accurately call variants occurring in <0.05% of a population. Using simulated and real datasets (viral, bacterial and human), we show that LoFreq has near-perfect specificity, with significantly improved sensitivity compared with existing methods and can efficiently analyze deep Illumina sequencing datasets without resorting to approximations or heuristics. We also present experimental validation for LoFreq on two different platforms (Fluidigm and Sequenom) and its application to call rare somatic variants from exome sequencing datasets for gastric cancer. Source code and executables for LoFreq are freely available at http://sourceforge.net/projects/lofreq/.     

ArrayPlex: distributed,interactive and programmatic access to genome sequence,annotation, ontology,and analytical toolsets

ArrayPlex is a software package that centrally provides a large number of flexible toolsets useful for functional genomics, including microarray data storage, quality assessments, data visualization, gene annotation retrieval, statistical tests, genomic sequence retrieval and motif analysis. It uses a client-server architecture based on open source components, provides graphical, command-line, and programmatic access to all needed resources, and is extensible by virtue of a documented application programming interface. ArrayPlex is available at http://sourceforge.net/projects/arrayplex/.     

cnvHiTSeq: integrative models for high-resolution copy number variation detection and genotyping using population sequencing data

Recent advances in sequencing technologies provide the means for identifying copy number variation (CNV) at an unprecedented resolution. A single next-generation sequencing experiment offers several features that can be used to detect CNV, yet current methods do not incorporate all available signatures into a unified model. cnvHiTSeq is an integrative probabilistic method for CNV discovery and genotyping that jointly analyzes multiple features at the population level. By combining evidence from complementary sources, cnvHiTSeq achieves high genotyping accuracy and a substantial improvement in CNV detection sensitivity over existing methods, while maintaining a low false discovery rate. cnvHiTSeq is available at http://sourceforge.net/projects/cnvhitseq     

Allele-specific copy-number discovery from whole-genome and whole-exome sequencing

Copy-number variants (CNVs) are a major form of genetic variation and a risk factor for various human diseases, so it is crucial to accurately detect and characterize them. It is conceivable that allele-specific reads from high-throughput sequencing data could be leveraged to both enhance CNV detection and produce allele-specific copy number (ASCN) calls. Although statistical methods have been developed to detect CNVs using whole-genome sequence (WGS) and/or whole-exome sequence (WES) data, information from allele-specific read counts has not yet been adequately exploited. In this paper, we develop an integrated method, called AS-GENSENG, which incorporates allele-specific read counts in CNV detection and estimates ASCN using either WGS or WES data. To evaluate the performance of AS-GENSENG, we conducted extensive simulations, generated empirical data using existing WGS and WES data sets and validated predicted CNVs using an independent methodology. We conclude that AS-GENSENG not only predicts accurate ASCN calls but also improves the accuracy of total copy number calls, owing to its unique ability to exploit information from both total and allele-specific read counts while accounting for various experimental biases in sequence data. Our novel, user-friendly and computationally efficient method and a complete analytic protocol is freely available at https://sourceforge.net/projects/asgenseng/.     

Virmid: accurate detection of somatic mutations with sample impurity inference

Detection of somatic variation using sequence from disease-control matched data sets is a critical first step. In many cases including cancer, however, it is hard to isolate pure disease tissue, and the impurity hinders accurate mutation analysis by disrupting overall allele frequencies. Here, we propose a new method, Virmid, that explicitly determines the level of impurity in the sample, and uses it for improved detection of somatic variation. Extensive tests on simulated and real sequencing data from breast cancer and hemimegalencephaly demonstrate the power of our model. A software implementation of our method is available at http://sourceforge.net/projects/virmid/.     

HeurAA: Accurate and Fast Detection of Genetic Variations with a Novel Heuristic Amplicon Aligner Program for Next Generation Sequencing

Next generation sequencing (NGS) of PCR amplicons is a standard approach to detect genetic variations in personalized medicine such as cancer diagnostics. Computer programs used in the NGS community often miss insertions and deletions (indels) that constitute a large part of known human mutations. We have developed HeurAA, an open source, heuristic amplicon aligner program. We tested the program on simulated datasets as well as experimental data from multiplex sequencing of 40 amplicons in 12 oncogenes collected on a 454 Genome Sequencer from lung cancer cell lines. We found that HeurAA can accurately detect all indels, and is more than an order of magnitude faster than previous programs. HeurAA can compare reads and reference sequences up to several thousand base pairs in length, and it can evaluate data from complex mixtures containing reads of different gene-segments from different samples. HeurAA is written in C and Perl for Linux operating systems, the code and the documentation are available for research applications at http://sourceforge.net/projects/heuraa/     

Characterization and identification of hidden rare variants in the human genome

Background

By examining the genotype calls generated by the 1000 Genomes Project we discovered that the human reference genome GRCh37 contains almost 20,000 loci in which the reference allele has never been observed in healthy individuals and around 70,000 loci in which it has been observed only in the heterozygous state.

Results

We show that a large fraction of this rare reference allele (RRA) loci belongs to coding, functional and regulatory elements of the genome and could be linked to rare Mendelian disorders as well as cancer. We also demonstrate that classical germline and somatic variant calling tools are not capable to recognize the rare allele when present in these loci. To overcome such limitations, we developed a novel tool, named RAREVATOR, that is able to identify and call the rare allele in these genomic positions. By using a small cancer dataset we compared our tool with two state-of-the-art callers and we found that RAREVATOR identified more than 1,500 germline and 22 somatic RRA variants missed by the two methods and which belong to significantly mutated pathways.

Conclusions

These results show that, to date, the investigation of around 100,000 loci of the human genome has been missed by re-sequencing experiments based on the GRCh37 assembly and that our tool can fill the gap left by other methods. Moreover, the investigation of the latest version of the human reference genome, GRCh38, showed that although the GRC corrected almost all insertions and a small part of SNVs and deletions, a large number of functionally relevant RRAs still remain unchanged. For this reason, also future resequencing experiments, based on GRCh38, will benefit from RAREVATOR analysis results. RAREVATOR is freely available at http://sourceforge.net/projects/rarevator.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1481-9) contains supplementary material, which is available to authorized users.     

Trait-Associated SNPs Are More Likely to Be eQTLs: Annotation to Enhance Discovery from GWAS

Although genome-wide association studies (GWAS) of complex traits have yielded more reproducible associations than had been discovered using any other approach, the loci characterized to date do not account for much of the heritability to such traits and, in general, have not led to improved understanding of the biology underlying complex phenotypes. Using a web site we developed to serve results of expression quantitative trait locus (eQTL) studies in lymphoblastoid cell lines from HapMap samples (http://www.scandb.org), we show that single nucleotide polymorphisms (SNPs) associated with complex traits (from http://www.genome.gov/gwastudies/) are significantly more likely to be eQTLs than minor-allele-frequency–matched SNPs chosen from high-throughput GWAS platforms. These findings are robust across a range of thresholds for establishing eQTLs (p-values from 10⁻⁴–10⁻⁸), and a broad spectrum of human complex traits. Analyses of GWAS data from the Wellcome Trust studies confirm that annotating SNPs with a score reflecting the strength of the evidence that the SNP is an eQTL can improve the ability to discover true associations and clarify the nature of the mechanism driving the associations. Our results showing that trait-associated SNPs are more likely to be eQTLs and that application of this information can enhance discovery of trait-associated SNPs for complex phenotypes raise the possibility that we can utilize this information both to increase the heritability explained by identifiable genetic factors and to gain a better understanding of the biology underlying complex traits.