The BLAST programs are widely used tools for searching protein and DNA databases for sequence similarities. For protein comparisons, a variety of definitional, algorithmic and statistical refinements described here permits the execution time of the BLAST programs to be decreased substantially while enhancing their sensitivity to weak similarities. A new criterion for triggering the extension of word hits, combined with a new heuristic for generating gapped alignments, yields a gapped BLAST program that runs at approximately three times the speed of the original. In addition, a method is introduced for automatically combining statistically significant alignments produced by BLAST into a position-specific score matrix, and searching the database using this matrix. The resulting Position-Specific Iterated BLAST (PSIBLAST) program runs at approximately the same speed per iteration as gapped BLAST, but in many cases is much more sensitive to weak but biologically relevant sequence similarities. PSI-BLAST is used to uncover several new and interesting members of the BRCT superfamily.

/pdf/gapped-blast-and-psi-blast-a-new-generation-of-protein-2ybl67x5yx.pdf

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.

Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.

/pdf/gene-ontology-tool-for-the-unification-of-biology-ppsktrcye3.pdf

Gene Ontology: tool for the unification of biology

We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

/pdf/mega7-molecular-evolutionary-genetics-analysis-version-7-0-1asv9rsrcf.pdf

MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets

MicroRNAs: Genomics, Biogenesis, Mechanism, and Function

We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.

https://www.science-open.com/document_file/00d79e6c-98e4-425c-9fd1-3a85d5fdefbc/PubMedCentral/00d79e6c-98e4-425c-9fd1-3a85d5fdefbc.pdf

MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

Programs such as MFOLD and ViennaRNA are widely used to predict RNA secondary structures How do these algorithms work? Why can't they predict RNA pseudoknots? How accurate are they, and will they get better?

How do RNA folding algorithms work

Amino acid sequence motif of group I intron endonucleases is conserved in open reading frames of group II introns

Rod and cone photoreceptors are highly similar in many respects but they have important functional and molecular differences. Here, we investigate genome-wide patterns of DNA methylation and chromatin accessibility in mouse rods and cones and correlate differences in these features with gene expression, histone marks, transcription factor binding, and DNA sequence motifs. Loss of NR2E3 in rods shifts their epigenomes to a more cone-like state. The data further reveal wide differences in DNA methylation between retinal photoreceptors and brain neurons. Surprisingly, we also find a substantial fraction of DNA hypo-methylated regions in adult rods that are not in active chromatin. Many of these regions exhibit hallmarks of regulatory regions that were active earlier in neuronal development, suggesting that these regions could remain undermethylated due to the highly compact chromatin in mature rods. This work defines the epigenomic landscapes of rods and cones, revealing features relevant to photoreceptor development and function.

/pdf/epigenomic-landscapes-of-retinal-rods-and-cones-5gmmhq8sfp.pdf

Epigenomic landscapes of retinal rods and cones

https://www.cell.com/current-biology/pdf/S0960-9822(12)01154-2.pdf

The C-value paradox, junk DNA and ENCODE

Many tools are available to analyse genomes but are often challenging to use in a cell type–specific context. We have developed a method similar to the isolation of nuclei tagged in a specific cell type (INTACT) technique [Deal,R.B. and Henikoff,S. (2010) A simple method for gene expression and chromatin profiling of individual cell types within a tissue. Dev. Cell, 18, 1030–1040; Steiner,F.A., Talbert,P.B., Kasinathan,S., Deal,R.B. and Henikoff,S. (2012) Cell-type-specific nuclei purification from whole animals for genome-wide expression and chromatin profiling. Genome Res., doi:10.1101/gr.131748.111], first developed in plants, for use in Drosophila neurons. We profile gene expression and histone modifications in Kenyon cells and octopaminergic neurons in the adult brain. In addition to recovering known gene expression differences, we also observe significant cell type–specific chromatin modifications. In particular, a small subset of differentially expressed genes exhibits a striking anti-correlation between repressive and activating histone modifications. These genes are enriched for transcription factors, recovering those known to regulate mushroom body identity and predicting analogous regulators of octopaminergic neurons. Our results suggest that applying INTACT to specific neuronal populations can illuminate the transcriptional regulatory networks that underlie neuronal cell identity.

Sean R. Eddy

Papers

How do RNA folding algorithms work

Amino acid sequence motif of group I intron endonucleases is conserved in open reading frames of group II introns

Epigenomic landscapes of retinal rods and cones

The C-value paradox, junk DNA and ENCODE

Cell type-specific genomics of Drosophila neurons.