Large scale identification and categorization of protein sequences using structured logistic regression.

BACKGROUND: Structured Logistic Regression (SLR) is a newly developed machine learning tool first proposed in the context of text categorization. Current availability of extensive protein sequence databases calls for an automated method to reliably classify sequences and SLR seems well-suited for this task. The classification of P-type ATPases, a large family of ATP-driven membrane pumps transporting essential cations, was selected as a test-case that would generate important biological information as well as provide a proof-of-concept for the application of SLR to a large scale bioinformatics problem. RESULTS: Using SLR, we have built classifiers to identify and automatically categorize P-type ATPases into one of 11 pre-defined classes. The SLR-classifiers are compared to a Hidden Markov Model approach and shown to be highly accurate and scalable. Representing the bulk of currently known sequences, we analysed 9.3 million sequences in the UniProtKB and attempted to classify a large number of P-type ATPases. To examine the distribution of pumps on organisms, we also applied SLR to 1,123 complete genomes from the Entrez genome database. Finally, we analysed the predicted membrane topology of the identified P-type ATPases. CONCLUSIONS: Using the SLR-based classification tool we are able to run a large scale study of P-type ATPases. This study provides proof-of-concept for the application of SLR to a bioinformatics problem and the analysis of P-type ATPases pinpoints new and interesting targets for further biochemical characterization and structural analysis.

A multi-platform draft de novo genome assembly and comparative analysis for the Scarlet Macaw (Ara macao).

Data deposition to NCBI Genomes: This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession AMXX00000000 (SMACv1.0, unscaffolded genome assembly). The version described in this paper is the first version (AMXX01000000). The scaffolded assembly (SMACv1.1) has been deposited at DDBJ/EMBL/GenBank under the accession AOUJ00000000, and is also the first version (AOUJ01000000). Strong biological interest in traits such as the acquisition and utilization of speech, cognitive abilities, and longevity catalyzed the utilization of two next-generation sequencing platforms to provide the first-draft de novo genome assembly for the large, new world parrot Ara macao (Scarlet Macaw). Despite the challenges associated with genome assembly for an outbred avian species, including 951,507 high-quality putative single nucleotide polymorphisms, the final genome assembly (>1.035 Gb) includes more than 997 Mb of unambiguous sequence data (excluding N's). Cytogenetic analyses including ZooFISH revealed complex rearrangements associated with two scarlet macaw macrochromosomes (AMA6, AMA7), which supports the hypothesis that translocations, fusions, and intragenomic rearrangements are key factors associated with karyotype evolution among parrots. In silico annotation of the scarlet macaw genome provided robust evidence for 14,405 nuclear gene annotation models, their predicted transcripts and proteins, and a complete mitochondrial genome. Comparative analyses involving the scarlet macaw, chicken, and zebra finch genomes revealed high levels of nucleotide-based conservation as well as evidence for overall genome stability among the three highly divergent species. Application of a new whole-genome analysis of divergence involving all three species yielded prioritized candidate genes and noncoding regions for parrot traits of interest (i.e., speech, intelligence, longevity) which were independently supported by the results of previous human GWAS studies. We also observed evidence for genes and noncoding loci that displayed extreme conservation across the three avian lineages, thereby reflecting their likely biological and developmental importance among birds.

Dissection of symbiosis and organ development by integrated transcriptome analysis of lotus japonicus mutant and wild-type plants.

Genetic analyses of plant symbiotic mutants has led to the identification of key genes involved in Rhizobium-legume communication as well as in development and function of nitrogen fixing root nodules. However, the impact of these genes in coordinating the transcriptional programs of nodule development has only been studied in limited and isolated studies. Here, we present an integrated genome-wide analysis of transcriptome landscapes in Lotus japonicus wild-type and symbiotic mutant plants. Encompassing five different organs, five stages of the sequentially developed determinate Lotus root nodules, and eight mutants impaired at different stages of the symbiotic interaction, our data set integrates an unprecedented combination of organ- or tissue-specific profiles with mutant transcript profiles. In total, 38 different conditions sampled under the same well-defined growth regimes were included. This comprehensive analysis unravelled new and unexpected patterns of transcriptional regulation during symbiosis and organ development. Contrary to expectations, none of the previously characterized nodulins were among the 37 genes specifically expressed in nodules. Another surprise was the extensive transcriptional response in whole root compared to the susceptible root zone where the cellular response is most pronounced. A large number of transcripts predicted to encode transcriptional regulators, receptors and proteins involved in signal transduction, as well as many genes with unknown function, were found to be regulated during nodule organogenesis and rhizobial infection. Combining wild type and mutant profiles of these transcripts demonstrates the activation of a complex genetic program that delineates symbiotic nitrogen fixation. The complete data set was organized into an indexed expression directory that is accessible from a resource database, and here we present selected examples of biological questions that can be addressed with this comprehensive and powerful gene expression data set.