Details on Vardict, a new variant caller, have been published recently in Nucleic Acids Research by authors, Zhongwu Lai and Jonathan Dry, among others from AstraZeneca . The current version of the Bina Read Alignment Variant Calling and Expression software module for secondary analysis includes Vardict, along with five other tools, for calling SNVs or indels from tumor-normal pairs. As the article demonstrates, VarDict has multiple strengths that extend our capabilities in variant calling beyond what was available before its release. Notably, the algorithm is particularly good at detecting indels. It also handles ultra-deep sequenced samples, which have become more common of late, and supports variant calling in tumor-only samples (in addition to tumor-vs-normal calling).
Within the Bina solution, you can use VarDict alone to identify somatic variants from whole-genome, whole-exome or targeted panels using tumor-normal pairs, or you can use it for calling variants in tumor-only samples.
In an upcoming release of the Bina RAVE software module as part of the Bina Genomic Management Solution (Bina GMS), we will include SomaticSeq, a machine-learning based algorithm that allows users to integrate VarDict, along with five other somatic callers, JointSNVMix , MuTect , SomaticIndelDetector , SomaticSniper  and VarScan2  to create a classifier that calls variants with higher accuracy than what may be achieved using a single caller. Users can upload a set of samples that match the type of samples they are working with as the training set in order to create a custom SomaticSeq classifier.
It’s worth noting that the inclusion of VarDict among the tools used to create the corresponding SomaticSeq classifier attributed to Bina’s number one ranking in Stage 5 of the DREAM Somatic Mutation Challenge last year for indel calling [7,8].
If you want to learn as much as you can about VarDict, we encourage you to read the new publication, as well as our SomaticSeq publication which compares the performance of VarDict with a number of other tools.
To learn more about SomaticSeq, register for our upcoming webinar in June.
 Lai Z, Markovets A, Ahdesmaki M, et al. Lai, Zhongwu, et al.VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research. Nucl. Acids Res. (2016).
 Fang LT, Afshar PT, Chhibber A, et al. An ensemble approach to accurately detect somatic mutations using SomaticSeq. Genome Biol. 2015;16:197-210.
 Roth A, Ding J, Morin R, et al. JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Bioinformatics.2012;28(7):907-13.
 Cibulskis K, Lawrence MS, Carter SL, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213-9.
 Larson DE, Harris CC, Chen K, et al. SomaticSniper: identification of somatic point mutations in whole genome sequencing data. Bioinformatics. 2012;28(3):311-7.
 Koboldt DC, Zhang Q, Larson DE, et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012;22(3):568-76.
 Roche Sequencing (2015) Bina places 1st and 2nd in DREAM Challenge. [Press Release]. Retreived from http://sequencing.roche.com/news---media/press-releases/bina-places-1st-and-2nd-in-dream-challenge.html. Accessed September 30, 2015.
 “8.5 Synthetic Challenge 5 Leaderboard” Sage Bionetworks. https://www.synapse.org/#!Synapse:syn312572/wiki/72943. Retrieved September, 30, 2015.