Tissue-Specific Alternative Splicing Analysis Reveals the Diversity of Chromosome 18 Transcriptome

Shargunov, A. V.; Krasnov, George S.; Ponomarenko, Elena A.; Lisitsa, Andrey; Shurdov, Mikhail A.; Zverev, Vitaly; Archakov, Alexander I.; Блинов, В. М.

doi:10.1021/pr400808u

Cited by 12 publications

(16 citation statements)

References 76 publications

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“…Even if inferences of in silico and in vitro assays on splicing were always correct, this does not necessarily imply that such variation in the splicing process is relevant in a given individual and tissue, let alone that it is disease-causing. Such thinking ignores the natural functions of alternative splicing and how little we still know about ethnic, gender, age and tissue variability of splicing patterns ( Shargunov et al, 2014 ).…”

Section: Experimental Evidence Of Pathogenicitymentioning

confidence: 99%

Medical genomics: The intricate path from genetic variant identification to clinical interpretation

Quintáns

Ordóñez-Ugalde

Cacheiro

et al. 2014

Applied & Translational Genomics

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The field of medical genomics involves translating high throughput genetic methods to the clinic, in order to improve diagnostic efficiency and treatment decision making. Technical questions related to sample enrichment, sequencing methodologies and variant identification and calling algorithms, still need careful investigation in order to validate the analytical step of next generation sequencing techniques for clinical applications. However, the main foreseeable challenge will be interpreting the clinical significance of the variants observed in a given patient, as well as their significance for family members and for other patients.Every step in the variant interpretation process has limitations and difficulties, and its quote of contribution to false positive and false negative results. There is no single piece of evidence enough on its own to make firm conclusions on the pathogenicity and disease causality of a given variant.A plethora of automated analysis software tools is being developed that will enhance efficiency and accuracy. However a risk of misinterpretation could derive from biased biorepository content, facilitated by annotation of variant functional consequences using previous datasets stored in the same or linked repositories. In order to improve variant interpretation and avoid an exponential accumulation of confounding noise in the medical literature, the use of terms in a standard way should be sought and requested when reporting genetic variants and their consequences. Generally, stepwise and linear interpretation processes are likely to overrate some pieces of evidence while underscoring others. Algorithms are needed that allow a multidimensional, parallel analysis of diverse lines of evidence to be carried out by expert teams for specific genes, cellular pathways or disorders.

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Section: Experimental Evidence Of Pathogenicitymentioning

confidence: 99%

Medical genomics: The intricate path from genetic variant identification to clinical interpretation

Quintáns

Ordóñez-Ugalde

Cacheiro

et al. 2014

Applied & Translational Genomics

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“…1–10 Consequently, a gene can generate multiple transcripts, which, when translated, may show differences in their expression, functions, and interactions with other biological molecules. 11–24 Functional analysis is a major field in genomics and proteomics.…”

Section: Introductionmentioning

confidence: 99%

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Menon

Govindarajoo

et al. 2015

J. Proteome Res.

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Alternative splicing allows a single gene to produce multiple transcript-level splice isoforms from which the translated proteins may show differences in their expression and function. Identifying the major functional or canonical isoform is important for understanding gene and protein functions. Identification and characterization of splice isoforms is a stated goal of the HUPO Human Proteome Project and of neXtProt. Multiple efforts have catalogued splice isoforms as “dominant”, “principal”, or “major” isoforms based on expression or evolutionary traits. In contrast, we recently proposed highest connected isoforms (HCIs) as a new class of canonical isoforms that have the strongest interactions in a functional network and revealed their significantly higher (differential) transcript-level expression compared to nonhighest connected isoforms (NCIs) regardless of tissues/cell lines in the mouse. HCIs and their expression behavior in the human remain unexplored. Here we identified HCIs for 6157 multi-isoform genes using a human isoform network that we constructed by integrating a large compendium of heterogeneous genomic data. We present examples for pairs of transcript isoforms of ABCC3, RBM34, ERBB2, and ANXA7. We found that functional networks of isoforms of the same gene can show large differences. Interestingly, differential expression between HCIs and NCIs was also observed in the human on an independent set of 940 RNA-seq samples across multiple tissues, including heart, kidney, and liver. Using proteomic data from normal human retina and placenta, we showed that HCIs are a promising indicator of expressed protein isoforms exemplified by NUDFB6 and M6PR. Furthermore, we found that a significant percentage (20%, p = 0.0003) of human and mouse HCIs are homologues, suggesting their conservation between species. Our identified HCIs expand the repertoire of canonical isoforms and are expected to facilitate studying main protein products, understanding gene regulation, and possibly evolution. The network is available through our web server as a rich resource for investigating isoform functional relationships (http://guanlab.ccmb.med.umich.edu/hisonet). All MS/MS data were available at ProteomeXchange Web site (http://www.proteomexchange.org) through their identifiers (retina: PXD001242, placenta: PXD000754).

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“…The studies [ 36 , 37 ] reveal the possibilities of using proteomic methods and mass-spectrometry for medicine and a search for biological markers. The studies [ 38 , 39 ] discuss the expression of potentially medically important genes at the proteomic level based on the analysis of genomic and transcriptomic data.…”

Section: Resultsmentioning

confidence: 99%

Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed

Ilgisonis

Lisitsa

Kudryavtseva

et al. 2018

Advances in Bioinformatics

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Concept-centered semantic maps were created based on a text-mining analysis of PubMed using the BiblioEngine_v2018 software. The objects (“concepts”) of a semantic map can be MeSH-terms or other terms (names of proteins, diseases, chemical compounds, etc.) structured in the form of controlled vocabularies. The edges between the two objects were automatically calculated based on the index of semantic similarity, which is proportional to the number of publications related to both objects simultaneously. On the one hand, an individual semantic map created based on the already published papers allows us to trace scientific inquiry. On the other hand, a prospective analysis based on the study of PubMed search history enables us to determine the possible directions for future research.

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Tissue-Specific Alternative Splicing Analysis Reveals the Diversity of Chromosome 18 Transcriptome

Cited by 12 publications

References 76 publications

Medical genomics: The intricate path from genetic variant identification to clinical interpretation

Medical genomics: The intricate path from genetic variant identification to clinical interpretation

Functional Networks of Highest-Connected Splice Isoforms: From The Chromosome 17 Human Proteome Project

Creation of Individual Scientific Concept-Centered Semantic Maps Based on Automated Text-Mining Analysis of PubMed

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