AltORFev facilitates the prediction of alternative open reading frames in eukaryotic mRNAs

Кочетов, А. В.; Allmer, Jens; Klimenko, Alexandra I.; Zuraev, Bulat S.; Matushkin, Yury G.; Lashin, Sergey A.

doi:10.1093/bioinformatics/btw736

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…An additional orthogonal, but poorly supported, neoantigen source is the proteasome, which was found to be capable of creating novel antigens by splicing peptides from diverse proteins to create a single antigen [156]. Several computational tools exist to predict post-translational modifications and alternative translation events from sequencing data, such as GPS [157] and KinasePhos [158] for phosphorylation events and altORFev [159] for alternative ORFs. To determine the immunogenicity of these alternative peptides, any tumor-specific predicted sequences could be input into neoantigen prediction software.…”

Section: Discussionmentioning

confidence: 99%

Best practices for bioinformatic characterization of neoantigens for clinical utility

et al. 2019

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Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor–normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

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Section: Discussionmentioning

confidence: 99%

Best practices for bioinformatic characterization of neoantigens for clinical utility

et al. 2019

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“…Furthermore, a clear connection between ASD and mTOR-mediated translational control also attracts attention to other related mechanisms (e.g., alternative ORFs, eIF2a-mediated control, nonAUG started uORFs, IRES, etc.) that may interfere in some particular cases [46][47][48].…”

Section: Discussionmentioning

confidence: 99%

The mTOR Signaling Pathway Activity and Vitamin D Availability Control the Expression of Most Autism Predisposition Genes

Trifonova

Klimenko

Мустафин

et al. 2019

IJMS

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Autism spectrum disorder (ASD) has a strong and complex genetic component with an estimate of more than 1000 genes implicated cataloged in SFARI (Simon′s Foundation Autism Research Initiative) gene database. A significant part of both syndromic and idiopathic autism cases can be attributed to disorders caused by the mechanistic target of rapamycin (mTOR)-dependent translation deregulation. We conducted gene-set analyses and revealed that 606 out of 1053 genes (58%) included in the SFARI Gene database and 179 out of 281 genes (64%) included in the first three categories of the database (“high confidence”, “strong candidate”, and “suggestive evidence”) could be attributed to one of the four groups: 1. FMRP (fragile X mental retardation protein) target genes, 2. mTOR signaling network genes, 3. mTOR-modulated genes, 4. vitamin D3 sensitive genes. The additional gene network analysis revealed 43 new genes and 127 new interactions, so in the whole 222 out of 281 (79%) high scored genes from SFARI Gene database were connected with mTOR signaling activity and/or dependent on vitamin D3 availability directly or indirectly. We hypothesized that genetic and/or environment mTOR hyperactivation, including provocation by vitamin D deficiency, might be a common mechanism controlling the expressivity of most autism predisposition genes and even core symptoms of autism.

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“…Accurate determination of the PO is a crucial step to verify the trinucleotide periodicity of ribosomes along coding regions [ 1 , 17 ], derive reliable translation initiation and elongation rates [ 18 , 19 ], accurately estimate codon usage bias and translation pauses [ 15 , 20 – 23 ], and reveal novel translated regions in known protein coding transcripts or ncRNAs [ 8 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data

et al. 2018

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Ribosome profiling is a powerful technique used to study translation at the genome-wide level, generating unique information concerning ribosome positions along RNAs. Optimal localization of ribosomes requires the proper identification of the ribosome P-site in each ribosome protected fragment, a crucial step to determine the trinucleotide periodicity of translating ribosomes, and draw correct conclusions concerning where ribosomes are located. To determine the P-site within ribosome footprints at nucleotide resolution, the precise estimation of its offset with respect to the protected fragment is necessary. Here we present riboWaltz, an R package for calculation of optimal P-site offsets, diagnostic analysis and visual inspection of ribosome profiling data. Compared to existing tools, riboWaltz shows improved accuracies for P-site estimation and neat ribosome positioning in multiple case studies. riboWaltz was implemented in R and is available as an R package at https://github.com/LabTranslationalArchitectomics/RiboWaltz.

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AltORFev facilitates the prediction of alternative open reading frames in eukaryotic mRNAs

Abstract: ak@bionet.nsc.ru.

Cited by 12 publications

References 15 publications

Best practices for bioinformatic characterization of neoantigens for clinical utility

Best practices for bioinformatic characterization of neoantigens for clinical utility

The mTOR Signaling Pathway Activity and Vitamin D Availability Control the Expression of Most Autism Predisposition Genes

riboWaltz: Optimization of ribosome P-site positioning in ribosome profiling data

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