GeneFEAST: the pivotal, gene-centric step in functional enrichment analysis interpretation.

Summary: GeneFEAST, implemented in Python, is a gene-centric functional enrichment analysis summarization and visualization tool that can be applied to large functional enrichment analysis (FEA) results arising from upstream FEA pipelines. It produces a systematic, navigable HTML report, making it easy to identify sets of genes putatively driving multiple enrichments and to explore gene-level quantitative data first used to identify input genes. Further, GeneFEAST can juxtapose FEA results from multiple studies, making it possible to highlight patterns of gene expression amongst genes that are differentially expressed in at least one of multiple conditions, and which give rise to shared enrichments under those conditions.

Mitotic DNA synthesis is caused by transcription-replication conflicts in BRCA2-deficient cells.

Aberrant replication causes cells lacking BRCA2 to enter mitosis with under-replicated DNA, which activates a repair mechanism known as mitotic DNA synthesis (MiDAS). Here, we identify genome-wide the sites where MiDAS reactions occur when BRCA2 is abrogated. High-resolution profiling revealed that these sites are different from MiDAS at aphidicolin-induced common fragile sites in that they map to genomic regions replicating in the early S-phase, which are close to early-firing replication origins, are highly transcribed, and display R-loop-forming potential.

Evaluation of computational methods for human microbiome analysis using simulated data.

Background: Our understanding of the composition, function, and health implications of human microbiota has been advanced by high-throughput sequencing and the development of new genomic analyses. However, trade-offs among alternative strategies for the acquisition and analysis of sequence data remain understudied.

Methods: We assessed eight popular taxonomic profiling pipelines; MetaPhlAn2, metaMix, PathoScope 2.

In-Depth Genomic and Phenotypic Characterization of the Antarctic Psychrotolerant Strain sp. MPC6 Reveals Unique Metabolic Features, Plasticity, and Biotechnological Potential.

We obtained the complete genome sequence of the psychrotolerant extremophile sp. MPC6, a natural Polyhydroxyalkanoates (PHAs) producing bacterium able to rapidly grow at low temperatures. Genomic and phenotypic analyses allowed us to situate this isolate inside the phylogroup of pseudomonads as well as to reveal its metabolic versatility and plasticity.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies.

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs.

Genome sequence of two members of the chloroaromatic-degrading MT community: Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3.

We describe the genome sequence of Pseudomonas reinekei MT1 and Achromobacter xylosoxidans MT3, the most abundant members of a bacterial community capable of degrading chloroaromatic compounds. The MT1 genome contains open reading frames encoding enzymes responsible for the catabolism of chlorosalicylate, methylsalicylate, chlorophenols, phenol, benzoate, p-coumarate, phenylalanine, and phenylacetate. On the other hand, the MT3 strain genome possesses no ORFs to metabolize chlorosalicylates; instead the bacterium is capable of metabolizing nitro-phenolic and phenolic compounds, which can be used as the only carbon and energy source by MT3.

Computational Methods for Human Microbiome Analysis.

As the field of microbiomics advances, the burden of computational work that scientists need to perform in order to extract biological insight has grown accordingly. Likewise, while human microbiome analyses are increasingly shifting toward a greater integration of various high-throughput data types, a core number of methods form the basis of nearly every study. In this unit, we present step-by-step protocols for five core stages of human microbiome research.