DNA data bank of Japan (DDBJ) progress report

Pseudomonas aeruginosa Type III Secretory Toxin ExoU and Its Predicted Homologs

Pseudomonas aeruginosa ExoU, a type III secretory toxin and major virulence factor with patatin-like phospholipase activity, is responsible for acute lung injury and sepsis in immunocompromised patients. Through use of a recently updated bacterial genome database, protein sequences predicted to be homologous to Ps. aeruginosa ExoU were identified in 17 other Pseudomonas species (Ps. fluorescens, Ps. lundensis, Ps. weihenstephanensis, Ps. marginalis, Ps. rhodesiae, Ps. synxantha, Ps. libanensis, Ps. extremaustralis, Ps. veronii, Ps. simiae, Ps. trivialis, Ps. tolaasii, Ps. orientalis, Ps. taetrolens, Ps. syringae, Ps. viridiflava, and Ps. cannabina) and 8 Gram-negative bacteria from three other genera (Photorhabdus, Aeromonas, and Paludibacterium). In the alignment of the predicted primary amino acid sequences used for the phylogenetic analyses, both highly conserved and nonconserved parts of the toxin were discovered among the various species. Further comparative studies of the predicted ExoU homologs should provide us with more detailed information about the unique characteristics of the Ps. aeruginosa ExoU toxin.

DNA Data Bank of Japan

The DNA Data Bank of Japan (DDBJ) (http://www.ddbj.nig.ac.jp) has been providing public data services for thirty years (since 1987). We are collecting nucleotide sequence data from researchers as a member of the International Nucleotide Sequence Database Collaboration (INSDC, http://www.insdc.org), in collaboration with the US National Center for Biotechnology Information (NCBI) and European Bioinformatics Institute (EBI). The DDBJ Center also services Japanese Genotype-phenotype Archive (JGA), with the National Bioscience Database Center to collect human-subjected data from Japanese researchers. Here, we report our database activities for INSDC and JGA over the past year, and introduce retrieval and analytical services running on our supercomputer system and their recent modifications. Furthermore, with the Database Center for Life Science, the DDBJ Center improves semantic web technologies to integrate and to share biological data, for providing the RDF version of the sequence data.

Pollen DNA barcoding: current applications and future prospects

Identification of the species origin of pollen has many applications, including assessment of plant–pollinator networks, reconstruction of ancient plant communities, product authentication, allergen monitoring, and forensics. Such applications, however, have previously been limited by microscopy-based identification of pollen, which is slow, has low taxonomic resolution, and has few expert practitioners. One alternative is pollen DNA barcoding, which could overcome these issues. Recent studies demonstrate that both chloroplast and nuclear barcoding markers can be amplified from pollen. These recent validations of pollen metabarcoding indicate that now is the time for researchers in various fields to consider applying these methods to their research programs. In this paper, we review the nascent field of pollen DNA barcoding and discuss potential new applications of this technology, highlighting existing limitations and future research developments that will improve its utility in a wide range of applications.

Molecular developmental mechanism in polypterid fish provides insight into the origin of vertebrate lungs

The lung is an important organ for air breathing in tetrapods and originated well before the terrestrialization of vertebrates. Therefore, to better understand lung evolution, we investigated lung development in the extant basal actinopterygian fish Senegal bichir (Polypterus senegalus). First, we histologically confirmed that lung development in this species is very similar to that of tetrapods. We also found that the mesenchymal expression patterns of three genes that are known to play important roles in early lung development in tetrapods (Fgf10, Tbx4, and Tbx5) were quite similar to those of tetrapods. Moreover, we found a Tbx4 core lung mesenchyme-specific enhancer (C-LME) in the genomes of bichir and coelacanth (Latimeria chalumnae) and experimentally confirmed that these were functional in tetrapods. These findings provide the first molecular evidence that the developmental program for lung was already established in the common ancestor of actinopterygians and sarcopterygians.

A MapReduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset

BACKGROUND

In the age of information superhighway, big data play a significant role in information processing, extractions, retrieving and management. In computational biology, the continuous challenge is to manage the biological data. Data mining techniques are sometimes imperfect for new space and time requirements. Thus, it is critical to process massive amounts of data to retrieve knowledge. The existing software and automated tools to handle big data sets are not sufficient. As a result, an expandable mining technique that enfolds the large storage and processing capability of distributed or parallel processing platforms is essential.

METHOD

In this analysis, a contemporary distributed clustering methodology for imbalance data reduction using k-nearest neighbor (K-NN) classification approach has been introduced. The pivotal objective of this work is to illustrate real training data sets with reduced amount of elements or instances. These reduced amounts of data sets will ensure faster data classification and standard storage management with less sensitivity. However, general data reduction methods cannot manage very big data sets. To minimize these difficulties, a MapReduce-oriented framework is designed using various clusters of automated contents, comprising multiple algorithmic approaches.

RESULTS

To test the proposed approach, a real DNA (deoxyribonucleic acid) dataset that consists of 90 million pairs has been used. The proposed model reduces the imbalance data sets from large-scale data sets without loss of its accuracy.

CONCLUSIONS

The obtained results depict that MapReduce based K-NN classifier provided accurate results for big data of DNA.