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Vinicius Pellisoli Machado

dblp:319/5594 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-2255-8182ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 1 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › proteomics
proteogenomics
0.612022
µProteInS - a proteogenomics pipeline for finding novel bacterial microproteins encoded by small ORFs · Bioinform. 2022

Methods — techniques the papers use, named apart from their topics

spectrum filtering · 0.6mass spectrometry · 0.6
YearPublicationVenuePosition
2022 µProteInS - a proteogenomics pipeline for finding novel bacterial microproteins encoded by small ORFs
abstract
SUMMARY: Genome annotation pipelines traditionally exclude open reading frames (ORFs) shorter than 100 codons to avoid false identifications. However, studies have been showing that these may encode functional microproteins with meaningful biological roles. We developed µProteInS, a proteogenomics pipeline that combines genomics, transcriptomics and proteomics to identify novel microproteins in bacteria. Our pipeline employs a model to filter out low confidence spectra, to avoid the need for manually inspecting Mass Spectrometry data. It also overcomes the shortcomings of traditional approaches that usually exclude overlapping genes, leaderless transcripts and non-conserved sequences, characteristics that are common among small ORFs (smORFs) and hamper their identification. AVAILABILITY AND IMPLEMENTATION: µProteInS is implemented in Python 3.8 within an Ubuntu 20.04 environment. It is an open-source software distributed under the GNU General Public License v3, available as a command-line tool. It can be downloaded at https://github.com/Eduardo-vsouza/uproteins and either installed from source or executed as a Docker image. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Eduardo Vieira de Souza, Pedro Ferrari Dalberto, Vinicius Pellisoli Machado, Adriana Canedo, Alan Saghatelian, Pablo Machado, Luiz Augusto Basso, Cristiano Valim Bizarro
Bioinform.3