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Li Tai Fang

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SomaticSeq: An Ensemble Approach with Machine Learning to Accurately Detect Somatic Mutations

Posted by Li Tai Fang on Oct 22, 2015 10:00:00 AM

This is the third article in a series. The first post discussed challenges in somatic mutation detection with respect to false positives and false negatives. The second post reviewed how a concensus approach might increase the confidence of the call sets from multiple tumor-normal callers. 

We developed SomaticSeq, an integrative machine learning pipeline, to address the limitations of current approaches [1]. SomaticSeq currently incorporates five somatic mutation callers, and uses machine learning (Adaptive Boosting model) to distinguish true mutations from false positives based on over 70 genomic and sequencing features. Using SomaticSeq, we have recently placed #1 in INDEL (F1 score of 71.6%) and #2 in SNV (F1 score of 99.7%) in Stage 5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. SomaticSeq is released under BSD open-source license at http://bioinform.github.io/somaticseq/.    

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Topics: DREAM Challenge Results, Cancer, Tumor-Normal, SomaticSeq

A Consensus Approach to Somatic Mutation Detection: When the Whole is Greater Than the Sum of Its Parts

Posted by Li Tai Fang on Oct 15, 2015 9:00:00 AM

In the previous post, we discussed challenges in somatic mutation detection with respect to false positives and false negatives. The research community probably is well aware of the aforementioned problems, and that is why the “naive subtract” method is rarely practiced. Instead, there is a plethora of specialized somatic mutation callers that analyze the tumor and normal genomes simultaneously to find somatic mutations. We benchmarked and selected  the best and most popular somatic mutation callers, namely MuTect [1], VarScan2 [2], JointSNVMix2 [3], SomaticSniper [4], and VarDict [5], for our ensemble calling approach.

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Topics: Somatic Mutation Detection, Cancer, SomaticSeq

False positives and negatives in cancer sequencing: When naive subtraction is just not enough

Posted by Li Tai Fang on Oct 6, 2015 10:00:00 AM

In cancer research, it is common to search for somatic mutations that appear in the tumor but not in the healthy tissues. Thus, tumor-normal comparisons have become the norm in somatic mutation detection in DNA sequencing. In an ideal world the workflow sounds simple: compare the tumor sequencing data against the normal. If a variant was found in the tumor but not found in the normal, it’s a somatic mutation. 

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Topics: DREAM Challenge Results, Cancer, Tumor-Normal, Bina RAVE, SomaticSeq

CNVkit and Seq2C for Targeted Sequencing

Posted by Li Tai Fang on Aug 25, 2015 12:00:00 PM

To improve detection of copy number aberration (CNA) in targeted sequencing, we have added Seq2C and CNVkit to version 2.6 of the Bina RAVE software module. These tools take advantage of population statistics, with an optional pool of normal controls, to call somatic CNAs in tumor targeted sequencing data.

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Topics: Product, Bina Tools & Workflows, RAVE

Detecting Copy Number Aberrations in Targeted Sequencing

Posted by Li Tai Fang on Aug 11, 2015 9:00:00 AM

A copy number gain or loss in a gene can be a critical driver event in carcinogenesis or cancer progression [1]. For instance, a copy number gain in HER2 gene is a driver event in many breast, lung, and colorectal cancers, and has important clinical implications that may be predictive of drug response [2,3,4]. As we have described previously, a copy number aberration (CNA) event leaves behind a variety of signatures in whole genome sequencing (WGS) data, such as read depth, B-allele frequency, soft-clipped reads and discordant reads. These signatures can be used as evidence by detection algorithms for calling CNAs.

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Topics: Bina Tools & Workflows, Secondary Analysis, Copy Number Aberration

Detecting CNAs for High-Coverage WGS Data (>60X) - New Tool in Bina RAVE 2.5

Posted by Li Tai Fang on Jun 2, 2015 8:30:00 AM

In addition to Control-FREEC that was incorporated into Bina RAVE™ for improving copy number aberrations (CNA) detection, we'd like to share with you another tool added to version 2.5 of the software in this post.

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Topics: Somatic Mutation Detection, Secondary Analysis, Tumor-Normal, RAVE, Copy Number Aberration

Detecting CNA Events in WGS With or Without a Normal Control - New in Bina RAVE 2.5

Posted by Li Tai Fang on May 15, 2015 9:00:00 AM

Previously we've shared our top contenders for evidence indicative of a Copy Number Aberration (CNA) occurrence in whole genome sequencing. In this post, we'll examine one of the tools added to version 2.5 of the Bina RAVE™ software module to help improve detection sensitivity.  

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Topics: Bina Tools & Workflows, RAVE, Copy Number Aberration

The “INDEL FINAL 4”: What Are the Best Types of Evidence Indicative of a CNA Occurrence in Whole Genome Sequencing?

Posted by Li Tai Fang on Apr 29, 2015 11:00:00 AM

March Madness is over, and with the Final Four behind us, I’ve been thinking about how scientists pick top contenders when we are selecting the tools we depend on for genomic analysis.  At Bina, one important thing our scientists think about when making this choice is “Level of Evidence.” One great example of this is how we look for Copy Number Aberrations (CNAs) in whole-genome sequencing (WGS). What type of evidence do we look for?

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Topics: Somatic Mutation Detection, Secondary Analysis, Tumor-Normal, RAVE

The Complexity of Somatic Mutation Detection, and Bina's Solution

Posted by Li Tai Fang on Sep 2, 2014 6:23:00 PM

We have recently released version 2.1 of the Bina genome analysis software, which includes a brand new tumor-normal somatic workflow.

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Topics: Product, Somatic Mutation Detection, Bina Tools & Workflows, Secondary Analysis, Cancer, Tumor-Normal

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