“One code to find them all”: a perl tool to conveniently parse RepeatMasker output files

International audienceBackground: Of the different bioinformatic methods used to recover transposable elements (TEs) in genome sequences, one of the most commonly used procedures is the homology-based method proposed by the RepeatMasker program. RepeatMasker generates several output files, including the .out file, which provides annotations for all detected repeats in a query sequence. However, a remaining challenge consists of identifying the different copies of TEs that correspond to the identified hits. This step is essential for any evolutionary/comparative analysis of the different copies within a family. Different possibilities can lead to multiple hits corresponding to a unique copy of an element, such as the presence of large deletions/insertions or undetermined bases, and distinct consensus corresponding to a single full-length sequence (like for long terminal repeat (LTR)-retrotransposons). These possibilities must be taken into account to determine the exact number of TE copies. Results: We have developed a perl tool that parses the RepeatMasker .out file to better determine the number and positions of TE copies in the query sequence, in addition to computing quantitative information for the different families. To determine the accuracy of the program, we tested it on several RepeatMasker .out files corresponding to two organisms (Drosophila melanogaster and Homo sapiens) for which the TE content has already been largely described and which present great differences in genome size, TE content, and TE families. Conclusions: Our tool provides access to detailed information concerning the TE content in a genome at the family level from the .out file of RepeatMasker. This information includes the exact position and orientation of each copy, its proportion in the query sequence, and its quality compared to the reference element. In addition, our tool allows a user to directly retrieve the sequence of each copy and obtain the same detailed information at the family level when a local library with incomplete TE class/subclass information was used with RepeatMasker. We hope that this tool will be helpful for people working on the distribution and evolution of TEs within genomes

Investigating the design space of smartwatches combining physical rotary inputs

Watches benefit from a long design history. Designers and engineers have successfully built devices using rotary physical inputs such as crowns, bezels, and wheels, separately or combined. Smart watch designers have explored the use of some of these inputs for interactions. However, a systematic exploration of their combinations has yet to be done. We investigate the design space of interactions with multiple rotary inputs through a three stages exploration. (1) We build upon observations of a collection of 113 traditional or electronic watches to propose a typology of physical rotary inputs for watches. (2) We conduct two focus groups to explore combination of physical rotary inputs. (3) We then build upon the output of these focus groups to design a low fidelity prototype, and further discuss the potential and challenges of rotary inputs combinations during a third focus group

Status of the BMV experiment

In this contribution we present the status of the BMV experiment whose goal is to measure the vacuum magnetic birefringence

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach

<p>Abstract</p> <p>Background</p> <p>Transcriptional gene regulation is one of the most important mechanisms in controlling many essential cellular processes, including cell development, cell-cycle control, and the cellular response to variations in environmental conditions. Genes are regulated by transcription factors and other genes/proteins via a complex interconnection network. Such regulatory links may be predicted using microarray expression data, but most regulation models suppose transcription factor independence, which leads to spurious links when many genes have highly correlated expression levels.</p> <p>Results</p> <p>We propose a new algorithm to infer combinatorial control networks from gene-expression data. Based on a simple model of combinatorial gene regulation, it includes a message-passing approach which avoids explicit sampling over putative gene-regulatory networks. This algorithm is shown to recover the structure of a simple artificial cell-cycle network model for baker's yeast. It is then applied to a large-scale yeast gene expression dataset in order to identify combinatorial regulations, and to a data set of direct medical interest, namely the Pleiotropic Drug Resistance (PDR) network.</p> <p>Conclusions</p> <p>The algorithm we designed is able to recover biologically meaningful interactions, as shown by recent experimental results <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Moreover, new cases of combinatorial control are predicted, showing how simple models taking this phenomenon into account can lead to informative predictions and allow to extract more putative regulatory interactions from microarray databases.</p

Diversity of Archaeosine Synthesis in Crenarchaeota

Archaeosine (G+) is found at position 15 of many archaeal tRNAs. In Euryarchaeota, the G+ precursor, 7-cyano-7-deazaguanine (preQ0), is inserted into tRNA by tRNA-guanine transglycosylase (arcTGT) before conversion into G+ by ARChaeosine Synthase (ArcS). However, many Crenarchaeota known to harbor G+ lack ArcS homologs. Using comparative genomics approaches, two families that could functionally replace ArcS in these organisms were identified: 1) GAT-QueC, a two-domain family with an N-terminal glutamine amidotransferase class-II domain fused to a domain homologous to QueC, the enzyme that produces preQ0; 2) QueF-like, a family homologous to the bacterial enzyme catalyzing the reduction of preQ0 to 7- aminomethyl-7-deazaguanine. Here we show that these two protein families are able to catalyze the formation of G+ in a heterologous system. Structure and sequence comparisons of crenarchaeal and euryarchaeal arcTGTs suggest the crenarchaeal enzymes have broader substrate specificity. These results led to a new model for the synthesis and salvage of G+ in Crenarchaeota

Functional Promiscuity of the COG0720 Family

The biosynthesis of GTP derived metabolites such as tetrahydrofolate (THF), biopterin (BH4), and the modified tRNA nucleosides queuosine (Q) and archaeosine (G+) relies on several enzymes of the Tunnel-fold superfamily. A subset of these proteins include the 6-pyruvoyl-tetrahydropterin (PTPS-II), PTPS-III, and PTPS-I homologs, all members of the COG0720 family, that have been previously shown to transform 7,8-dihydroneopterin triphosphate (H2NTP) into different products. PTPS-II catalyzes the formation of 6-pyruvoyltetrahydropterin in the BH4 pathway. PTPS-III catalyzes the formation of 6-hydroxylmethyl-7,8-dihydropterin in the THF pathway. PTPS-I catalyzes the formation of 6-carboxy-5,6,7,8-tetrahydropterin in the Q pathway. Genes of these three enzyme families are often misannotated as they are difficult to differentiate by sequence similarity alone. Using a combination of physical clustering, signature motif, and phylogenetic codistribution analyses, in vivo complementation studies, and in vitro enzymatic assays, a complete reannotation of the COG0720 family was performed in prokaryotes. Notably, this work identified and experimentally validated dual function PTPS-I/III enzymes involved in both THF and Q biosynthesis. Both in vivo and in vitro analyses showed that the PTPS-I family could tolerate a translation of the active site cysteine and was inherently promiscuous, catalyzing different reactions on the same substrate, or the same reaction on different substrates. Finally, the analysis and experimental validation of several archaeal COG0720 members confirmed the role of PTPS-I in archaeosine biosynthesis, and resulted in the identification PTPS-III enzymes with variant signature sequences in Sulfolobus species. This study reveals an expanded versatility of the COG0720 family members and illustrates that for certain protein families, extensive comparative genomic analysis beyond homology is required to correctly predict function