Massive Sequencing Data Research Articles

PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data

Paired-End Mapper (PEMer) enables mapping of genomic structural variants at considerably enhanced sensitivity, specificity and resolution over previous approaches.

Toward a more complete view of tRNA biology

Transfer RNAs are ancient molecules present in all domains of life. In addition to translating the genetic code into protein and defining the second genetic code together with aminoacyl-tRNA synthetases, tRNAs act in many other cellular functions. Robust phenomenological observations on the role of tRNAs in translation, together with massive sequence and crystallographic data, have led to a deeper physicochemical understanding of tRNA architecture, dynamics and identity. In vitro studies complemented by cell biology data already indicate how tRNA behaves in cellular environments, in particular in higher Eukarya. From an opposite approach, reverse evolution considerations suggest how tRNAs emerged as simplified structures from the RNA world. This perspective discusses what basic questions remain unanswered, how these answers can be obtained and how a more rational understanding of the function and dysfunction of tRNA can have applications in medicine and biotechnology.

Resolving Animal Phylogeny: A Sledgehammer for a Tough Nut?

While great progress toward discovering the evolutionary relationships of major animal groups has been made in the past 2 decades, significant portions of the animal tree remain unresolved. A recent manuscript in Nature by Dunn and colleagues tackles this problem using massive sequence data sets from many taxa-the so-called "phylogenomic" approach.

A Transcriptomic and Proteomic Characterization of the Arabidopsis Mitochondrial Protein Import Apparatus and Its Response to Mitochondrial Dysfunction

Mitochondria import hundreds of cytosolically synthesized proteins via the mitochondrial protein import apparatus. Expression analysis in various organs of 19 components of the Arabidopsis mitochondrial protein import apparatus encoded by 31 genes showed that although many were present in small multigene families, often only one member was prominently expressed. This was supported by comparison of real-time reverse transcriptase-polymerase chain reaction and microarray experimental data with expressed sequence tag numbers and massive parallel signature sequence data. Mass spectrometric analysis of purified mitochondria identified 17 import components, their mitochondrial sub-compartment, and verified the presence of TIM8, TIM13, TIM17, TIM23, TIM44, TIM50, and METAXIN proteins for the first time, to our knowledge. Mass spectrometry-detected isoforms correlated with the most abundant gene transcript measured by expression data. Treatment of Arabidopsis cell culture with mitochondrial electron transport chain inhibitors rotenone and antimycin A resulted in a significant increase in transcript levels of import components, with a greater increase observed for the minor isoforms. The increase was observed 12 h after treatment, indicating that it was likely a secondary response. Microarray analysis of rotenone-treated cells indicated the up-regulation of gene sets involved in mitochondrial chaperone activity, protein degradation, respiratory chain assembly, and division. The rate of protein import into isolated mitochondria from rotenone-treated cells was halved, even though rotenone had no direct effect on protein import when added to mitochondria isolated from untreated cells. These findings suggest that transcription of import component genes is induced when mitochondrial function is limited and that minor gene isoforms display a greater response than the predominant isoforms.

Information enhancement methods for large scale sequence analysis

The improved efficiency of similarity search programs and the affordability of even faster computers allow studies where whole sequence databases can be the target of various comparisons with increasingly larger or numerous query sequences. However, the usefulness of those “brute force” methods now becomes limited by the time it takes an experienced scientist to sift the biologically relevant matches from overwhelming, albeit “statistically significant” outputs. The discrepancy between statistical vs biological significance has different causes: erroneous database entries, repetitive sequence elements, and the ubiquity of low complexity segments with biased composition. We present two masking methods (programs XNU and XBLAST) capable of eliminating most of the irrelevant outputs in a variety of large scale sequence analysis situations: global “all against all” database comparisons, massive partial cDNA sequencing (EST), positional cloning and genomic data analysis.