PubMed Highlight: a compass in the vast sea of NGS analysis tools

One of the most productive field in NGS related research is for sure data analysis tools, starting from raw sequence data tools down to variant annotation and prioritization softwares. Tons of different projects has been made available for free developed by academic research groups and several others have been generated by commercial companies. As usual, every software has its strengths and pitfalls and it is best suited for a specific application. However, some are for sure better then others and trying to orient in this vast sea of tools could be frustrating.

Luckily experienced had came at help providing this two brief but complete surveys on error-correcting methods and exome variant analysis tools. Sure a useful compass to help navigate in the NGS sea! Take a look!

A survey of error-correction methods for next-generation sequencing.

Brief Bioinform. 2013 Jan;14(1):56-66. doi: 10.1093/bib/bbs015. Epub 2012 Apr 6.

Yang X, Chockalingam SP, Aluru S. 

Abstract

Error Correction is important for most next-generation sequencing applications because highly accurate sequenced reads will likely lead to higher quality results. Many techniques for error correction of sequencing data from next-gen platforms have been developed in the recent years. However, compared with the fast development of sequencing technologies, there is a lack of standardized evaluation procedure for different error-correction methods, making it difficult to assess their relative merits and demerits. In this article, we provide a comprehensive review of many error-correction methods, and establish a common set of benchmark data and evaluation criteria to provide a comparative assessment. We present experimental results on quality, run-time, memory usage and scalability of several error-correction methods. Apart from providing explicit recommendations useful to practitioners, the review serves to identify the current state of the art and promising directions for future research. Availability: All error-correction programs used in this article are downloaded from hosting websites. The evaluation tool kit is publicly available at: http://aluru-sun.ece.iastate.edu/doku.php?id=ecr.

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

Brief Bioinform. 2013 Jan 21

Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, Krabichler B, Speicher MR, Zschocke J, Trajanoski Z. 

Abstract

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.