Posts Tagged ‘funseq’

PLOS Genetics: A Massively Parallel Pipeline to Clone DNA Variants and Examine Molecular Phenotypes of Human Disease Mutations

February 7, 2015

Massively Parallel Pipeline to Clone DNA Variants & Examine…Disease
Mutations http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004819 CloneSeq leverages NextGen sequencing

With the advance of sequencing technologies, tens of millions of genomic variants have been discovered in the human population. However, there is no available method to date that is capable of determining the functional impact of these variants on a large scale, which has increasingly become a huge bottleneck for the development of population genetics and personal genomics. Clone-seq and comparative interactome-profiling pipeline is a first to address this issue.

Can be coupled to many readouts.

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ, Sunyaev SR. Pooled association tests for rare variants in exon-resequencing studies. American Journal of Human Genetics (2010) 86: 832-838.

February 1, 2015

Pooled association tests for rare variants in exon-resequencing http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3032073 Simulation shows advantage of mult. rarity thresholds

Price AL, Kryukov GV, de Bakker PI, Purcell SM, Staples J, Wei LJ,
Sunyaev SR. Pooled association tests for rare variants in
exon-resequencing studies. American Journal of Human Genetics (2010)
86: 832-838.

SUMMARY

Multiple studies indicate strong association between rare variants and
resulting phenotype. This paper describes a population-genetics
simulation framework to study the influence of variant allele
frequency on the corresponding phenotype. In a prior study, causal
relationship between variants and phenotype was resolved by performing
association test on set of variants having allele frequency below a
fixed threshold. However, here it is observed that simulation
frameworks based on a variable allele frequency threshold provide
higher accuracy in association test compared to the fixed allele
frequency model. In addition, inclusion of predicted functional
effects of variants (Polyphen-2 scores) increases the accuracy of the
variable frequency threshold model. Overall, this paper describes a novel methodology, which can be
used to explore the association between rare variants and various
diseases.

Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types : Nature Genetics : Nature Publishing Group

January 8, 2015

Analysis of noncoding somatic mutations &…expression alterations http://www.nature.com/ng/journal/v46/n12/full/ng.3141.html 505 WGS variants w. RNAseq, #TCGA as of Mar ’14

all of what’s in TCGA as of spring ’14

505 TCGA WGS Somatic mutations, Expression Calls, CNA
via
https://www.synapse.org/#!Synapse:syn2882200

Orthogonal to PCAWG-607 (Alexandrov et al + 100 "public" stomach cancers)

Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the “Guilt-by-Asso ciation” Principle

August 7, 2014

Inheritance Modes in… #Network Challenges… Guilt-by-Association http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3710751 #Diseases of recessive interface SNVs predictable

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3710751/

surprisingly, no positional effects on LOF mutations … significant proportion of truncation alleles give rise to functional products

“guilt by assoc works”

signif. number dom mut give rise to func products

Oncotator

August 4, 2014

http://www.broadinstitute.org/oncotator
https://github.com/broadinstitute/oncotator

Useful listing of data sources, viz:

QT:{{”

Protein Annotations

Site-specific protein annotations from UniProt.
Druggable target data from DrugBank.
Functional impact predictions from PolyPhen-2.

Cancer Annotations

Observed cancer mutation frequency annotations from COSMIC.
Cancer gene and mutation annotations from the Cancer Gene Census. Significant amplification/deletion region annotations from Tumorscape and theTCGA Copy Number Portal.
Overlapping Oncomap mutations from the Cancer Cell Line Encyclopedia. Significantly mutated gene annotations aggregated from published MutSiganalyses. Cancer gene annotations from the Familial Cancer Database.
Human DNA Repair Gene annotations from Wood et al.

“}}

Uses bamboo testing software
https://www.atlassian.com/software/bamboo

Three-dimensional reconstruction of protein networks provides insight into human genetic disease : Nature Biotechnology : Nature Publishing Group

July 19, 2014

http://www.nature.com/nbt/journal/v30/n2/full/nbt.2106.html

Dissecting Disease Inheritance Modes in a Three-Dimensional Protein Network Challenges the “Guilt-by-Asso ciation” Principle

July 19, 2014

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3710751/

surprisingly, no positional effects on LOF mutations … significant proportion of truncation alleles give rise to functional products

“guilt by assoc works”

signif. number dom mut give rise to func products

Phevor Combines Multiple Biomedical Ontologies for Accurate Identification of Disease-Causing Alleles in Single Individuals and Small Nuclear Families

May 9, 2014

http://www.cell.com/ajhg/abstract/S0002-9297(14)00112-8

Genome-wide signals of positive selection in human evolution

April 6, 2014

http://genome.cshlp.org/content/early/2014/03/11/gr.164822.113.abstract

“We further demonstrate that the observed signatures of positive selection correlate better with the presence of regulatory sequences, as predicted by the ENCODE Project Consortium, than with the positions of amino acid substitutions. Our results suggest that adaptation was frequent in human evolution and provide support for the hypothesis of King and Wilson that adaptive divergence is primarily driven by regulatory changes.”

Similar to conclusion positive-section section in FunSeq paper

PARADIGM-SHIFT predicts the function of mutations in multiple cancers using pathway impact analysis

March 3, 2014

PARADIGM-SHIFT predicts… function of mutations in… #cancers using pathway[s]. #Network-based gene prioritization
http://bioinformatics.oxfordjournals.org/content/28/18/i640