THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.     

BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

MendeLIMS: a web-based laboratory information management system for clinical genome sequencing

Background

Large clinical genomics studies using next generation DNA sequencing require the ability to select and track samples from a large population of patients through many experimental steps. With the number of clinical genome sequencing studies increasing, it is critical to maintain adequate laboratory information management systems to manage the thousands of patient samples that are subject to this type of genetic analysis.

Results

To meet the needs of clinical population studies using genome sequencing, we developed a web-based laboratory information management system (LIMS) with a flexible configuration that is adaptable to continuously evolving experimental protocols of next generation DNA sequencing technologies. Our system is referred to as MendeLIMS, is easily implemented with open source tools and is also highly configurable and extensible. MendeLIMS has been invaluable in the management of our clinical genome sequencing studies.

Conclusions

We maintain a publicly available demonstration version of the application for evaluation purposes at http://mendelims.stanford.edu. MendeLIMS is programmed in Ruby on Rails (RoR) and accesses data stored in SQL-compliant relational databases. Software is freely available for non-commercial use at http://dna-discovery.stanford.edu/software/mendelims/.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-290) contains supplementary material, which is available to authorized users.     

Comparative GO: A Web Application for Comparative Gene Ontology and Gene Ontology-Based Gene Selection in Bacteria

Availabilityhttp://turing.ersa.edu.au/BacteriaGO.     

PROCAIN: protein profile comparison with assisting information

Detection of remote sequence homology is essential for the accurate inference of protein structure, function and evolution. The most sensitive detection methods involve the comparison of evolutionary patterns reflected in multiple sequence alignments (MSAs) of protein families. We present PROCAIN, a new method for MSA comparison based on the combination of ‘vertical’ MSA context (substitution constraints at individual sequence positions) and ‘horizontal’ context (patterns of residue content at multiple positions). Based on a simple and tractable profile methodology and primitive measures for the similarity of horizontal MSA patterns, the method achieves the quality of homology detection comparable to a more complex advanced method employing hidden Markov models (HMMs) and secondary structure (SS) prediction. Adding SS information further improves PROCAIN performance beyond the capabilities of current state-of-the-art tools. The potential value of the method for structure/function predictions is illustrated by the detection of subtle homology between evolutionary distant yet structurally similar protein domains. ProCAIn, relevant databases and tools can be downloaded from: http://prodata.swmed.edu/procain/download. The web server can be accessed at http://prodata.swmed.edu/procain/procain.php.     

Genome Sequence of Aggregatibacter actinomycetemcomitans Serotype c Strain D11S-1

Aggregatibacter actinomycetemcomitans is a major etiological agent of periodontitis. Here we report the complete genome sequence of serotype c strain D11S-1, which was recovered from the subgingival plaque of a patient diagnosed with generalized aggressive periodontitis.Aggregatibacter actinomycetemcomitans is a major etiologic agent of human periodontal disease, in particular aggressive periodontitis (12). The natural population of A. actinomycetemcomitans is clonal (7). Six A. actinomycetemcomitans serotypes are distinguished based on the structural and serological characteristics of the O antigen of LPS (6, 7). Three of the serotypes (a, b, and c) comprise >80% of all strains, and each serotype represents a distinct clonal lineage (1, 6, 7). Serotype c strain D11S-1 was cultured from a subgingival plaque sample of a patient diagnosed with generalized aggressive periodontitis. The complete genome sequencing of the strain was determined by 454 pyrosequencing (10), which achieved 25× coverage. Assembly was performed using the Newbler assembler (454, Branford, CT) and generated 199 large contigs, with 99.3% of the bases having a quality score of 40 and above. The contigs were aligned with the genome of the sequenced serotype b strain HK1651 (http://www.genome.ou.edu/act.html) using software written in house. The putative contig gaps were then closed by primer walking and sequencing of PCR products over the gaps. The final genome assembly was further confirmed by comparison of an in silico NcoI restriction map to the experimental map generated by optical mapping (8). The genome structure of the D11S-1 strain was compared to that of the sequenced strain HK1651 using the program MAUVE (2, 3). The automated annotation was done using a protocol similar to the annotation engine service at The Institute for Genomic Research/J. Craig Venter Institute with some local modifications. Briefly, protein-coding genes were identified using Glimmer3 (4). Each protein sequence was then annotated by comparing to the GenBank nonredundant protein database. BLAST-Extend-Repraze was applied to the predicted genes to identify genes that might have been truncated due to a frameshift mutation or premature stop codon. tRNA and rRNA genes were identified by using tRNAScan-SE (9) and a similarity search to our in-house RNA database, respectively.The D11S-1 circular genome contains 2,105,764 nucleotides, a GC content of 44.55%, 2,134 predicted coding sequences, and 54 tRNA and 19 rRNA genes (see additional data at http://expression.washington.edu/bumgarnerlab/publications.php). The distribution of predicted genes based on functional categories was similar between D11S-1 and HK1651 (http://expression.washington.edu/bumgarnerlab/publications.php). One hundred six and 86 coding sequences were unique to strain D11S-1 and HK1651, respectively (http://expression.washington.edu/bumgarnerlab/publications.php). Genomic islands were identified based on annotations for strain HK1651 and based on manual inspection of contiguous D11S-1 specific DNA regions with G+C bias (http://expression.washington.edu/bumgarnerlab/publications.php). Among 12 identified genomics islands, 5 (B, C, D, E and G; cytolethal distending toxin gene cluster, tight adherence gene cluster, O-antigen biosynthesis and transport gene cluster, leukotoxin gene cluster, and lipoligosaccharide biosynthesis enzyme gene, respectively) correspond to islands 2 to 5 and 8 of strain HK1651 (http://www.oralgen.lanl.gov/) (5). Island F (∼5 kb) is homologous to a portion of the 12.5-kb island 7 in HK1651. Five genomic islands (H to L) were unique to strain D11S-1. The remaining island (A) is a fusion of genomic islands 1 and 6, in strain HK1651. The genome of D11S-1 is largely in synteny with the genome of the sequenced serotype b strain HK1651 but contained several large-scale genomic rearrangements.Strain D11S-1 harbors a 43-kb bacteriophage and two plasmids of 31 and 23 kb (http://expression.washington.edu/bumgarnerlab/publications.php). Excluding an ∼9-kb region of low homology, the phage showed >90% nucleotide sequence identity with AaΦ23 (11). A 49-bp attB site (11) was identified at coordinates 2,024,825 to 2,024,873. The location of the inserted phage was identified in the optical map of strain D11S-1 and further confirmed by PCR amplification and sequencing of the regions flanking the insertion site. A closed circular form of the phage was also detected in strain D11S-1 by PCR analysis of the phage ends. The 23-kb plasmid is homologous to pVT745 (92% nucleotide identities). The 31-kb plasmid is a novel plasmid. It has significant homologies in short regions (<2 kb) to Haemophilus influenzae biotype aegyptius plasmid pF1947 and other plasmids.     

Investigating microRNA-Target Interaction-Supported Tissues in Human Cancer Tissues Based on miRNA and Target Gene Expression Profiling

Recent studies have revealed that a small non-coding RNA, microRNA (miRNA) down-regulates its mRNA targets. This effect is regarded as an important role in various biological processes. Many studies have been devoted to predicting miRNA-target interactions. These studies indicate that the interactions may only be functional in some specific tissues, which depend on the characteristics of an miRNA. No systematic methods have been established in the literature to investigate the correlation between miRNA-target interactions and tissue specificity through microarray data. In this study, we propose a method to investigate miRNA-target interaction-supported tissues, which is based on experimentally validated miRNA-target interactions. The tissue specificity results by our method are in accordance with the experimental results in the literature.

Availability and Implementation

Our analysis results are available at http://tsmti.mbc.nctu.edu.tw/ and http://www.stat.nctu.edu.tw/hwang/tsmti.html.     

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research.

Availability

http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx     

SHEAR: sample heterogeneity estimation and assembly by reference

Background

Personal genome assembly is a critical process when studying tumor genomes and other highly divergent sequences. The accuracy of downstream analyses, such as RNA-seq and ChIP-seq, can be greatly enhanced by using personal genomic sequences rather than standard references. Unfortunately, reads sequenced from these types of samples often have a heterogeneous mix of various subpopulations with different variants, making assembly extremely difficult using existing assembly tools. To address these challenges, we developed SHEAR (Sample Heterogeneity Estimation and Assembly by Reference; http://vk.cs.umn.edu/SHEAR), a tool that predicts SVs, accounts for heterogeneous variants by estimating their representative percentages, and generates personal genomic sequences to be used for downstream analysis.

Results

By making use of structural variant detection algorithms, SHEAR offers improved performance in the form of a stronger ability to handle difficult structural variant types and better computational efficiency. We compare against the lead competing approach using a variety of simulated scenarios as well as real tumor cell line data with known heterogeneous variants. SHEAR is shown to successfully estimate heterogeneity percentages in both cases, and demonstrates an improved efficiency and better ability to handle tandem duplications.

Conclusion

SHEAR allows for accurate and efficient SV detection and personal genomic sequence generation. It is also able to account for heterogeneous sequencing samples, such as from tumor tissue, by estimating the subpopulation percentage for each heterogeneous variant.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-84) contains supplementary material, which is available to authorized users.     

From Gigabyte to Kilobyte: A Bioinformatics Protocol for Mining Large RNA-Seq Transcriptomics Data

RNA-Seq techniques generate hundreds of millions of short RNA reads using next-generation sequencing (NGS). These RNA reads can be mapped to reference genomes to investigate changes of gene expression but improved procedures for mining large RNA-Seq datasets to extract valuable biological knowledge are needed. RNAMiner—a multi-level bioinformatics protocol and pipeline—has been developed for such datasets. It includes five steps: Mapping RNA-Seq reads to a reference genome, calculating gene expression values, identifying differentially expressed genes, predicting gene functions, and constructing gene regulatory networks. To demonstrate its utility, we applied RNAMiner to datasets generated from Human, Mouse, Arabidopsis thaliana, and Drosophila melanogaster cells, and successfully identified differentially expressed genes, clustered them into cohesive functional groups, and constructed novel gene regulatory networks. The RNAMiner web service is available at http://calla.rnet.missouri.edu/rnaminer/index.html.     

Compression of next-generation sequencing reads aided by highly efficient de novo assembly

We present Quip, a lossless compression algorithm for next-generation sequencing data in the FASTQ and SAM/BAM formats. In addition to implementing reference-based compression, we have developed, to our knowledge, the first assembly-based compressor, using a novel de novo assembly algorithm. A probabilistic data structure is used to dramatically reduce the memory required by traditional de Bruijn graph assemblers, allowing millions of reads to be assembled very efficiently. Read sequences are then stored as positions within the assembled contigs. This is combined with statistical compression of read identifiers, quality scores, alignment information and sequences, effectively collapsing very large data sets to <15% of their original size with no loss of information. Availability: Quip is freely available under the 3-clause BSD license from http://cs.washington.edu/homes/dcjones/quip.     

NeSSM: A Next-Generation Sequencing Simulator for Metagenomics

Background

Metagenomics can reveal the vast majority of microbes that have been missed by traditional cultivation-based methods. Due to its extremely wide range of application areas, fast metagenome sequencing simulation systems with high fidelity are in great demand to facilitate the development and comparison of metagenomics analysis tools.

Results

We present here a customizable metagenome simulation system: NeSSM (Next-generation Sequencing Simulator for Metagenomics). Combining complete genomes currently available, a community composition table, and sequencing parameters, it can simulate metagenome sequencing better than existing systems. Sequencing error models based on the explicit distribution of errors at each base and sequencing coverage bias are incorporated in the simulation. In order to improve the fidelity of simulation, tools are provided by NeSSM to estimate the sequencing error models, sequencing coverage bias and the community composition directly from existing metagenome sequencing data. Currently, NeSSM supports single-end and pair-end sequencing for both 454 and Illumina platforms. In addition, a GPU (graphics processing units) version of NeSSM is also developed to accelerate the simulation. By comparing the simulated sequencing data from NeSSM with experimental metagenome sequencing data, we have demonstrated that NeSSM performs better in many aspects than existing popular metagenome simulators, such as MetaSim, GemSIM and Grinder. The GPU version of NeSSM is more than one-order of magnitude faster than MetaSim.

Conclusions

NeSSM is a fast simulation system for high-throughput metagenome sequencing. It can be helpful to develop tools and evaluate strategies for metagenomics analysis and it’s freely available for academic users at http://cbb.sjtu.edu.cn/~ccwei/pub/software/NeSSM.php.     

PBEAM: A parallel implementation of BEAM for genome-wide inference of epistatic interactions

The software tool PBEAM provides a parallel implementation of the BEAM, which is the first algorithm for large scale epistatic interaction mapping, including genome-wide studies with hundreds of thousands of markers. BEAM describes markers and their interactions with a Bayesian partitioning model and computes the posterior probability of each marker sets via Markov Chain Monte Carlo (MCMC). PBEAM takes the advantage of simulating multiple Markov chains simultaneously. This design can efficiently reduce ~n-fold execution time in the circumstance of n CPUs. The implementation of PBEAM is based on MPI libraries.

Availability

PBEAM is available for download at http://bioinfo.au.tsinghua.edu.cn/pbeam/     

Science Translator: An Interview with Louisa Stark

The Genetics Society of America’s Elizabeth W. Jones Award for Excellence in Education recognizes significant and sustained impact on genetics education. The 2015 awardee, Louisa Stark, has made a major impact on global access to genetics education through her work as director of the University of Utah Genetic Science Learning Center. The Center’s Learn.Genetics and Teach.Genetics websites are the most widely used online genetic education resources in the world. In 2014, they were visited by 18 million students, educators, scientists, and members of the public. With over 60 million page views annually, Learn.Genetics is among the most used sites on the Web.Open in a separate window     

Synapse-Assembly Proteins Maintain Synaptic Vesicle Cluster Stability and Regulate Synaptic Vesicle Transport in Caenorhabditis elegans

The functional integrity of neurons requires the bidirectional active transport of synaptic vesicles (SVs) in axons. The kinesin motor KIF1A transports SVs from somas to stable SV clusters at synapses, while dynein moves them in the opposite direction. However, it is unclear how SV transport is regulated and how SVs at clusters interact with motor proteins. We addressed these questions by isolating a rare temperature-sensitive allele of Caenorhabditis elegans unc-104 (KIF1A) that allowed us to manipulate SV levels in axons and dendrites. Growth at 20° and 14° resulted in locomotion rates that were ∼3 and 50% of wild type, respectively, with similar effects on axonal SV levels. Corresponding with the loss of SVs from axons, mutants grown at 14° and 20° showed a 10- and 24-fold dynein-dependent accumulation of SVs in their dendrites. Mutants grown at 14° and switched to 25° showed an abrupt irreversible 50% decrease in locomotion and a 50% loss of SVs from the synaptic region 12-hr post-shift, with no further decreases at later time points, suggesting that the remaining clustered SVs are stable and resistant to retrograde removal by dynein. The data further showed that the synapse-assembly proteins SYD-1, SYD-2, and SAD-1 protected SV clusters from degradation by motor proteins. In syd-1, syd-2, and sad-1 mutants, SVs accumulate in an UNC-104-dependent manner in the distal axon region that normally lacks SVs. In addition to their roles in SV cluster stability, all three proteins also regulate SV transport.     

Differential analysis of high-throughput quantitative genetic interaction data

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed ''differential interactions'', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.