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Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism

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TLDR
A new interactome mapping approach is introduced by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors, demonstrating the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.
Abstract
Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

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Journal ArticleDOI

The BioGRID interaction database: 2015 update

TL;DR: The BioGRID architecture has been improved to support a broader range of interaction and post-translational modification types, to allow the representation of more complex multi-gene/protein interactions, to account for cellular phenotypes through structured ontologies, to expedite curation through semi-automated text-mining approaches, and to enhance curation quality control.
Journal ArticleDOI

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder

Ryan K. C. Yuen, +89 more
- 06 Mar 2017 - 
TL;DR: Se sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal that identified 18 new candidate ASD-risk genes and found that participants bearing mutations in susceptibility genes had significantly lower adaptive ability.
References
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Journal ArticleDOI

The Sequence Alignment/Map format and SAMtools

TL;DR: SAMtools as discussed by the authors implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments.
Journal ArticleDOI

Base-calling of automated sequencer traces using Phred. I. accuracy assessment

TL;DR: In this article, a base-calling program for automated sequencer traces, phred, with improved accuracy was proposed. But it was not shown to achieve a lower error rate than the ABI software, averaging 40%-50% fewer errors in the data sets examined independent of position in read, machine running conditions, or sequencing chemistry.
Journal ArticleDOI

CAP3: A DNA Sequence Assembly Program

TL;DR: The third generation of the CAP sequence assembly program is described, which has a capability to clip 5' and 3' low-quality regions of reads and uses forward-reverse constraints to correct assembly errors and link contigs.
Journal ArticleDOI

Alternative Isoform Regulation in Human Tissue Transcriptomes

TL;DR: An in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments yielding a digital inventory of gene and mRNA isoform expression suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation.
Journal ArticleDOI

A General Framework for Weighted Gene Co-Expression Network Analysis

TL;DR: A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.
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