Macphersonbauer1973

Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL https//maayanlab.cloud/drugmonizome/.Finding relevant information from newly published scientific papers is becoming increasingly difficult due to the pace at which articles are published every year as well as the increasing amount of information per paper. Biocuration and model organism databases provide a map for researchers to navigate through the complex structure of the biomedical literature by distilling knowledge into curated and standardized information. In addition, scientific search engines such as PubMed and text-mining tools such as Textpresso allow researchers to easily search for specific biological aspects from newly published papers, facilitating knowledge transfer. However, digesting the information returned by these systems-often a large number of documents-still requires considerable effort. In this paper, we present Wormicloud, a new tool that summarizes scientific articles in a graphical way through word clouds. This tool is aimed at facilitating the discovery of new experimental results not yet curated by model organism databases and is designed for both researchers and biocurators. Wormicloud is customized for the Caenorhabditis elegans literature and provides several advantages over existing solutions, including being able to perform full-text searches through Textpresso, which provides more accurate results than other existing literature search engines. Wormicloud is integrated through direct links from gene interaction pages in WormBase. Additionally, it allows analysis on the gene sets obtained from literature searches with other WormBase tools such as SimpleMine and Gene Set Enrichment. Database URL https//wormicloud.textpressolab.com.

VCF files with results of sequencing projects take a lot of space. Sunitinib manufacturer We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets.

https//github.com/refresh-bio/vcfshark.

Supplementary data are available at publisher's Web site.

Supplementary data are available at publisher's Web site.

Dietary guidelines recommend limiting red meat intake because it is a major source of medium- and long-chain SFAs and is presumed to increase the risk of cardiovascular disease (CVD). Evidence of an association between unprocessed red meat intake and CVD is inconsistent.

The study aimed to assess the association of unprocessed red meat, poultry, and processed meat intake with mortality and major CVD.

The Prospective Urban Rural Epidemiology (PURE) Study is a cohort of 134,297 individuals enrolled from 21 low-, middle-, and high-income countries. Food intake was recorded using country-specific validated FFQs. The primary outcomes were total mortality and major CVD. HRs were estimated using multivariable Cox frailty models with random intercepts.

In the PURE study, during 9.5 y of follow-up, we recorded 7789 deaths and 6976 CVD events. Higher unprocessed red meat intake (≥250 g/wk vs. <50 g/wk) was not significantly associated with total mortality (HR 0.93; 95% CI 0.85, 1.02; P-trend=0.14) or major CVD (HR 1.01; 95% CI 0.92, 1.11; P-trend=0.72). Similarly, no association was observed between poultry intake and health outcomes. Higher intake of processed meat (≥150 g/wk vs. 0 g/wk) was associated with higher risk of total mortality (HR 1.51; 95% CI 1.08, 2.10; P-trend=0.009) and major CVD (HR 1.46; 95% CI 1.08, 1.98; P-trend=0.004).

In a large multinational prospective study, we did not find significant associations between unprocessed red meat and poultry intake and mortality or major CVD. Conversely, a higher intake of processed meat was associated with a higher risk of mortality and major CVD.

In a large multinational prospective study, we did not find significant associations between unprocessed red meat and poultry intake and mortality or major CVD. Conversely, a higher intake of processed meat was associated with a higher risk of mortality and major CVD.

As the next-generation sequencing technology becomes broadly applied, genomics and transcriptomics are becoming more commonly used in both research and clinical settings. However, proteomics is still an obstacle to be conquered. For most peptide search programs in proteomics, a standard reference protein database is used. Because of the thousands of coding DNA variants in each individual, a standard reference database does not provide perfect match for many proteins/peptides of an individual. A personalized reference database can improve the detection power and accuracy for individual proteomics data. To connect genomics and proteomics, we designed a Python package PrecisionProDB that is specialized for generating a personized protein database for proteomics applications. PrecisionProDB supports multiple popular file formats and reference databases, and can generate a personized database in minutes. To demonstrate the application of PrecisionProDB, we generated human population-specific reference protein databases with PrecisionProDB, which improves the number of identified peptides by 0.34% on average. In addition, by incorporating cell line-specific variants into the protein database, we demonstrated a 0.71% improvement for peptide identification in the Jurkat cell line. With PrecisionProDB and these datasets, researchers and clinicians can improve their peptide search performance by adopting the more representative protein database or adding population and individual-specific proteins to the search database with minimum increase of efforts.

PrecisionProDB and pre-calculated protein databases are freely available at https//github.com/ATPs/PrecisionProDB and https//github.com/ATPs/PrecisionProDB_references.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.