Archive for the 'SciLit' Category

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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.

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The landscape of long noncoding RNAs in the human transcriptome : Nature Genetics : Nature Publishing Group

January 28, 2015

Landscape of lncRNAs in the human #transcriptome
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3192.html Derived from RNAseq read assembly; not much overlap w/ @GencodeGenes

Matthew K Iyer,
Yashar S Niknafs,
Rohit Malik,
Udit Singhal,
Anirban Sahu,
Yasuyuki Hosono,
Terrence R Barrette,
John R Prensner,
Joseph R Evans,
Shuang Zhao,
Anton Poliakov,
Xuhong Cao,
Saravana M Dhanasekaran,
Yi-Mi Wu,
Dan R Robinson,
David G Beer,
Felix Y Feng,
Hariharan K Iyer
& Arul M Chinnaiyan

Nature Genetics (2015) doi:10.1038/ng.3192Received 20 June 2014 Accepted 18 December 2014

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Reconciling differential gene expression data with molecular interaction networks

January 28, 2015

Reconciling differential gene expression
w/…#networkshttp://bioinformatics.oxfordjournals.org/content/29/5/622 Propagating this across interactions finds perturbed pathways

This paper basically propagates scores of disease-related highly differentially expressed genes (-log10 p) over human protein interaction network, calculates new scores using four major algorithms (Vanilla, PageRank, GeneMANIA, Heat Kernel), re-ranks genes based on the new scores and then finds enriched pathways among top-ranking genes. Compared with traditional ways by ranking highly differentially expressed genes based on p-values without any network information, the approach not only recovered canonical pathways but also discovered novel ones such as an insulin-mediated glucose transport pathway in Huntington’s disease. The authors also explored differences among four algorithms and identified the top-ranking genes specifically found by particular algorithms. In short, the paper provides a valuable framework for integrating networks and gene expression data. Their analysis for comparing four major algorithms is also helpful.

http://bioinformatics.oxfordjournals.org/content/29/5/622

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Distributed Information Processing in Biological and Computational Systems

January 26, 2015

Distributed Info. Processing in Biological & Computational #Systems http://cacm.acm.org/magazines/2015/1/181614-distributed-information-processing-in-biological-and-computational-systems/fulltext Contrasts in strategies to handle node failures

QT:{{"
While both computational and biological systems need to address these similar types of failures, the methods they use to do so differs. In distributed computing, failures have primarily been handled by majority voting methods,37 by using dedicated failure detectors, or via cryptography. In contrast, most biological systems rely on various network topological features to handle failures. Consider for example the use of failure detectors. In distributed computing, these are either implemented in hardware or in dedicated additional software. In contrast, biology implements implicit failure detector mechanisms by relying on backup nodes or alternative pathways. Several proteins have paralogs, that is, structurally similar proteins that in most cases originated from the same ancestral protein (roughly 40% of yeast and human proteins have at least one paralog). In several cases, when one protein fails or is altered, its paralog can automatically take its place24 or protect the cell against the mutation.26 Thus, by preserving backup functionality in the protein interaction.

…
While we discussed some reoccurring algorithmic strategies used within both types of systems (for example, stochasticity and feedback), there is much more to learn in this regard. From the distributed computing side, new models are needed to address the dynamic aspects of communication (for example, nodes joining and leaving the network, and edges added and being subtracted), which are also relevant in mobile computing scenarios. Further, while the biological systems we discussed all operate without a single centralized controller, there is in fact a continuum in the term “distributed.” For example, hierarchical distributed models, where higher layers “control” lower layers with possible feedback, represent a more structured type of control system than traditional distributed systems without such a hierarchy. Gene regulatory networks and neuronal networks (layered columns) both share such a hierarchical structure, and this structure has been well-conserved across many different species, suggesting their importance to computation. Such models, however, have received less attention in the distributed computing literature.

"}}

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Pgenes make proteins

January 24, 2015

Bioinformatics (2015) 31 (1): 33-39. doi: 10.1093/bioinformatics/btu615

Making novel proteins from #pseudogenes
http://bioinformatics.oxfordjournals.org/content/31/1/33.short Outcomes in 16 cases where one gets stable & functional translated products

http://bioinformatics.oxfordjournals.org/content/31/1/33.short

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WASP: allele-specific software for robust discovery of molecular quantitative trait loci | bioRxiv

January 19, 2015

WASP: allele-specific software for robust discovery of molecular quantitative trait loci
Bryce van de Geijn, Graham McVicker, Yoav Gilad, Jonathan Pritchard

doi: http://dx.doi.org/10.1101/011221
http://biorxiv.org/content/early/2014/11/07/011221

QT:{{”
Mapping of reads to a reference genome is biased by sequence polymorphisms6. Reads which contain the non-reference allele may fail to map uniquely or map to a different (incorrect) location in the genome6.
“}}

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No evidence that selection has been less effective at removing deleterious mutations in Europeans than in Africans : Nature Genetics : Nature Publishing Group

January 18, 2015

Removing deleterious mutations in Europeans [v] Africans
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.3186.html Comparing nonsynonymous freq. betw. populations HT @obahcall

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Emerging landscape of oncogenic signatures across human cancers

January 17, 2015

Landscape of oncogenic signatures across human #cancers
http://www.nature.com/ng/journal/v45/n10/full/ng.2762.html Disjoint types dominated by copy number changes or mutations

pp1127 – 1133

Giovanni Ciriello, Martin L Miller, Bülent Arman Aksoy, Yasin Senbabaoglu, Nikolaus Schultz & Chris Sander

doi:10.1038/ng.2762

Chris Sander and colleagues have extracted significant functional events from 12 tumor types. Tumors can be classified as being driven largely by either mutation or copy number changes, and, within this division, subclasses of cross-tissue patterns of events are discerned that suggest sets of combinatorial therapies.

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Variation in cancer risk among tissues can be explained by the number of stem cell divisions

January 12, 2015

Tomasetti & Volgenstein

Science 2 January 2015:
Vol. 347 no. 6217 pp. 78-81
DOI: 10.1126/science.1260825

It’s a correlation between aggressiveness, mutations and cell division http://www.sciencemag.org/content/347/6217/78

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NEw paper using BrainSpan data

January 12, 2015

The discovery of integrated gene networks for autism and related disorders

Fereydoun Hormozdiari
Osnat Penn
Elhanan Borenstein
Evan E. Eichler

Published in Advance November 5, 2014, doi:10.1101/gr.178855.114 Genome Res. 2015. 25: 142-154

QT:{{”
Motivated by this observation, we have developed a novel method that simultaneously integrates information from both PPI and coexpression networks to identify highly connected modules in both types of networks that are also enriched in mutations in cases and not in controls. We call this method MAGI, short for merging affected genes into integrated networks. MAGI is based on a combinatorial
optimization algorithm that aims to maximize the number of mutations in the modules while accounting for gene length and distribution of putative LoF and missense mutations in cases and controls. MAGI is generic and can be applied to any disease, given a list of de novo mutations in cases and relevant coexpression information. Using neurodevelopmental RNA-seq data from the BrainSpan Atlas
(http://www.brainspan.org/), we have applied it to exome sequence data generated from ASD, ID, epilepsy, and schizophrenia, providing a comprehensive comparison of common and specific gene modules for these diseases.
“}}

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