Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers.

/pdf/a-survey-of-tools-for-variant-analysis-of-next-generation-1zl3y3vvrl.pdf

A survey of tools for variant analysis of next-generation genome sequencing data

The rapid expansion of next-generation sequencing has yielded a powerful array of tools to address fundamental biological questions at a scale that was inconceivable just a few years ago. Various genome-partitioning strategies to sequence select subsets of the genome have emerged as powerful alternatives to whole-genome sequencing in ecological and evolutionary genomic studies. High-throughput targeted capture is one such strategy that involves the parallel enrichment of preselected genomic regions of interest. The growing use of targeted capture demonstrates its potential power to address a range of research questions, yet these approaches have yet to expand broadly across laboratories focused on evolutionary and ecological genomics. In part, the use of targeted capture has been hindered by the logistics of capture design and implementation in species without established reference genomes. Here we aim to (i) increase the accessibility of targeted capture to researchers working in nonmodel taxa by discussing capture methods that circumvent the need of a reference genome, (ii) highlight the evolutionary and ecological applications where this approach is emerging as a powerful sequencing strategy and (iii) discuss the future of targeted capture and other genome-partitioning approaches in the light of the increasing accessibility of whole-genome sequencing. Given the practical advantages and increasing feasibility of high-throughput targeted capture, we anticipate an ongoing expansion of capture-based approaches in evolutionary and ecological research, synergistic with an expansion of whole-genome sequencing.

Targeted capture in evolutionary and ecological genomics

https://www.webaigo.it/download/1-s2.0-S0016508513011396-main.pdf

Genetic Alterations Associated With Progression From Pancreatic Intraepithelial Neoplasia to Invasive Pancreatic Tumor

We evaluated homologous recombination deficient (HRD) phenotypes in epithelial ovarian cancer (EOC) considering BRCA1, BRCA2, and RAD51C in a large well-annotated patient set. We evaluated EOC patients for germline deleterious mutations (n = 899), somatic mutations (n = 279) and epigenetic alterations (n = 482) in these genes using NGS and genome-wide methylation arrays. Deleterious germline mutations were identified in 32 (3.6%) patients for BRCA1, in 28 (3.1%) for BRCA2 and in 26 (2.9%) for RAD51C. Ten somatically sequenced patients had deleterious alterations, six (2.1%) in BRCA1 and four (1.4%) in BRCA2. Fifty two patients (10.8%) had methylated BRCA1 or RAD51C. HRD patients with germline or somatic alterations in any gene were more likely to be high grade serous, have an earlier diagnosis age and have ovarian and/or breast cancer family history. The HRD phenotype was most common in high grade serous EOC. Identification of EOC patients with an HRD phenotype may help tailor specific therapies.

/pdf/clinical-characteristics-of-ovarian-cancer-classified-by-4fy9fdisyx.pdf

Clinical Characteristics of Ovarian Cancer Classified by BRCA1, BRCA2, and RAD51C Status

Summary: TREAT (Targeted RE-sequencing Annotation Tool) is a tool for facile navigation and mining of the variants from both targeted resequencing and whole exome sequencing. It provides a rich integration of publicly available as well as in-house developed annotations and visualizations for variants, variant-hosting genes and host-gene pathways.

Availability and implementation: TREAT is freely available to non-commercial users as either a stand-alone annotation and visualization tool, or as a comprehensive workflow integrating sequencing alignment and variant calling. The executables, instructions and the Amazon Cloud Images of TREAT can be downloaded at the website: http://ndc.mayo.edu/mayo/research/biostat/stand-alone-packages.cfm

Contact: ude.oyam@fisA.niassoH; ude.oyam@erreiPnaeJ.rehcoK

Supplementary information: Supplementary data are provided at Bioinformatics online.

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data

Next-generation sequencing technology platforms are driving the development of a variety of approaches to study genomic variation associated with disease. One of these approaches, exome sequencing, specifically targets the coding regions of the genome, which are captured and sequenced. Compared with whole genome sequencing, exome sequencing offers the advantages of being cost- and time-effective while providing deeper coverage of coding variants, which are more likely to affect function.

However, the protocol is known to be only partially reliable and might miss some of the coding regions. To assess how much coding region could be missed or of target, we compared whole genome and exome sequencing data derived from one sample that was processed by the Illumina GA-IIx platform.

Our in-house-developed workflow named TREAT (Targeted RE-sequencing and Annotation Tool) was used to align and annotate the data. We provide a summary of the comparison between the two datasets, including the total number of reads produced, the time needed for sequencing and analysis, the coverage of coding regions and the agreement between called variants.

https://link.springer.com/content/pdf/10.1186%2F1465-6906-12-S1-P43.pdf

Genomes or exomes: evaluation of cost, time and coverage

BackgroundDNA methylation is an important epigenetic modification involved in many biological processes. Reduced representation bisulfite sequencing (RRBS) is a cost-effective method for studying DNA methylation at single base resolution. Although several tools are available for RRBS data processing and analysis, it is not clear which strategy performs the best and there has not been much attention to the contamination issue from artificial cytosines incorporated during the end repair step of library preparation. To address these issues, we describe a new method, Targeted Alignment and Artificial Cytosine Elimination for RRBS (TRACE-RRBS), which aligns bisulfite sequence reads to MSP1 digitally digested reference and specifically removes the end repair cytosines. We compared this approach on a simulated and a real dataset with 7 other RRBS analysis tools and Illumina 450 K microarray platform.ResultsTRACE-RRBS aligns sequence reads to a small fraction of the genome where RRBS protocol targets on and was demonstrated as the fastest, most sensitive and specific tool for the simulated dataset. For the real dataset, TRACE-RRBS took about the same time as RRBSMAP, a third to a sixth of time needed for BISMARK and NOVOALIGN. TRACE-RRBS aligned more reads uniquely than other tools and achieved the highest correlation with 450 k microarray data. The end repair artificial cytosine removal increased correlation between nearby CpGs and accuracy of methylation quantification.ConclusionsTRACE-RRBS is fast and more accurate tool for RRBS data analysis. It is freely available for academic use at http://bioinformaticstools.mayo.edu/.

Additional file 1: Table S1. of Targeted alignment and end repair elimination increase alignment and methylation measure accuracy for reduced representation bisulfite sequencing data

Jean-Pierre A. Kocher

Papers

TREAT: a bioinformatics tool for variant annotations and visualizations in targeted and exome sequencing data

Genomes or exomes: evaluation of cost, time and coverage

Additional file 1: Table S1. of Targeted alignment and end repair elimination increase alignment and methylation measure accuracy for reduced representation bisulfite sequencing data