RESUMO
We read with considerable interest the study by Gusenbauer and Haddaway (Gusenbauer and Haddaway, 2020, Research Synthesis Methods, doi:10.1002/jrsm.1378) comparing the systematic search qualities of 28 search systems, including Google Scholar (GS) and PubMed. Google Scholar and PubMed are the two most popular free academic search tools in biology and chemistry, with GS being the number one search tool in the world. Those academics using GS as their principal system for literature searches may be unaware of research which enumerates five critical features for scientific literature tools that greatly influenced Gusenbauer's 2020 study. Using this list as the framework for a targeted comparison between just GS and PubMed, we found stark differences which overwhelmingly favored PubMed. In this comment, we show that by comparing the characteristics of the two search tools, features that are particularly useful in one search tool, but are missing in the other, are strikingly spotlighted. One especially popular feature that ubiquitously appears in GS, but not in PubMed, is the forward citation search found under every citation as a clickable Cited by N link. We seek to improve the PubMed search experience using two approaches. First, we request that PubMed add Cited by N links, making them as omnipresent as the GS links. Second, we created an open-source command-line tool, pmidcite, which is used alongside PubMed to give information to researchers to help with the choice of the next paper to examine, analogous to how GS's Cited by N links help to guide users. Find pmidcite at https://github.com/dvklopfenstein/pmidcite.
Assuntos
Publicações , Ferramenta de Busca , Confiabilidade dos Dados , PubMed , Projetos de PesquisaRESUMO
Lysine methylation of histones and non-histone substrates by the SET domain containing protein lysine methyltransferase (KMT) G9a/EHMT2 governs transcription contributing to apoptosis, aberrant cell growth, and pluripotency. The positioning of chromosomes within the nuclear three-dimensional space involves interactions between nuclear lamina (NL) and the lamina-associated domains (LAD). Contact of individual LADs with the NL are dependent upon H3K9me2 introduced by G9a. The mechanisms governing the recruitment of G9a to distinct subcellular sites, into chromatin or to LAD, is not known. The cyclin D1 gene product encodes the regulatory subunit of the holoenzyme that phosphorylates pRB and NRF1 thereby governing cell-cycle progression and mitochondrial metabolism. Herein, we show that cyclin D1 enhanced H3K9 dimethylation though direct association with G9a. Endogenous cyclin D1 was required for the recruitment of G9a to target genes in chromatin, for G9a-induced H3K9me2 of histones, and for NL-LAD interaction. The finding that cyclin D1 is required for recruitment of G9a to target genes in chromatin and for H3K9 dimethylation, identifies a novel mechanism coordinating protein methylation.
Assuntos
Ciclina D1/metabolismo , Metilação de DNA/fisiologia , Antígenos de Histocompatibilidade/metabolismo , Histona-Lisina N-Metiltransferase/metabolismo , Histonas/metabolismo , Ciclo Celular/fisiologia , Linhagem Celular , Linhagem Celular Tumoral , Cromatina/metabolismo , Cromossomos/fisiologia , Células HEK293 , Humanos , Células MCF-7 , Ligação Proteica/fisiologiaRESUMO
The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology (GO) enrichment analysis may return hundreds of statistically significant GO results in a "flat" list, which can be challenging to summarize. It can also be difficult to keep pace with rapidly expanding biological knowledge, which often results in daily changes to any of the over 47,000 gene ontologies that describe biological knowledge. GOATOOLS, a Python-based library, makes it more efficient to stay current with the latest ontologies and annotations, perform gene ontology enrichment analyses to determine over- and under-represented terms, and organize results for greater clarity and easier interpretation using a novel GOATOOLS GO grouping method. We performed functional analyses on both stochastic simulation data and real data from a published RNA-seq study to compare the enrichment results from GOATOOLS to two other popular tools: DAVID and GOstats. GOATOOLS is freely available through GitHub: https://github.com/tanghaibao/goatools .