Using Publicly Available RNA-seq Data for Expression Analysis of Genes of Interest.

RNA-seq data in publicly available repositories enable the efficient reanalysis of transcript abundances in existing experiments. Graphical user interfaces usually only allow the visual inspection of a single gene and of predefined experiments. Here, we describe how experiments are selected from the Sequence Read Archive or the European Nucleotide Archive, how data is efficiently mapped onto a reference transcriptome, and how global transcript abundances and patterns are inspected. We exemplarily apply this analysis pipeline to study the expression of photorespiration-related genes in photosynthetic organisms, such as cyanobacteria, and to identify conditions under which photorespiratory transcript abundances are enhanced.

Whole-Genome Sequence of Pseudomonas sp. Strain MM211, Isolated from Soil in Langenfeld, Germany.

Here, we present the genome sequence of Pseudomonas sp. strain MM211, which was isolated from garden soil. The complete circular genome consists of a 5,281,862-bp chromosome, with a GC content of 61.5%.

Complete Genome Sequence of Pseudomonas sp. Strain MM213, an Isolate from a Brookside in Bielefeld, Germany.

Here, we report the genome sequence of Pseudomonas sp. strain MM213, isolated from brookside soil in Bielefeld, Germany. The genome is complete and consists of 6,746,355 bp, with a GC content of 59.4% and 6,145 predicted protein-coding sequences. Pseudomonas sp. strain MM213 is part of the Pseudomonas mandelii group.

Local DNA shape is a general principle of transcription factor binding specificity in Arabidopsis thaliana.

Understanding gene expression will require understanding where regulatory factors bind genomic DNA. The frequently used sequence-based motifs of protein-DNA binding are not predictive, since a genome contains many more binding sites than are actually bound and transcription factors of the same family share similar DNA-binding motifs. Traditionally, these motifs only depict sequence but neglect DNA shape. Since shape may contribute non-linearly and combinational to binding, machine learning approaches ought to be able to better predict transcription factor binding. Here we show that a random forest machine learning approach, which incorporates the 3D-shape of DNA, enhances binding prediction for all 216 tested Arabidopsis thaliana transcription factors and improves the resolution of differential binding by transcription factor family members which share the same binding motif. We observed that DNA shape features were individually weighted for each transcription factor, even if they shared the same binding sequence.