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\"What can we learn from the genes of our closest evolutionary relatives? Neanderthal Man tells the story of geneticist Svante Pääbo's mission to answer that question, beginning with the study of DNA in Egyptian mummies in the early 1980s and culminating in his sequencing of the Neanderthal genome in 2009. From Pääbo, we learn how Neanderthal genes offer a unique window into the lives of our hominin relatives and may hold the key to unlocking the mystery of why humans survived while Neanderthals went extinct. Drawing on genetic and fossil clues, Pääbo explores what is known about the origin of modern humans and their relationship to the Neanderthals and describes the fierce debate surrounding the nature of the two species' interactions. A riveting story about a visionary researcher and the nature of scientific inquiry, Neanderthal Man offers rich insight into the fundamental question of who we are\"-- Provided by publisher.

The number and diversity of viral sequences that are identified in metagenomic data far exceeds that of experimentally characterized virus isolates. In a recent workshop, a panel of experts discussed the proposal that, with appropriate quality control, viruses that are known only from metagenomic data can, and should be, incorporated into the official classification scheme of the International Committee on Taxonomy of Viruses (ICTV). Although a taxonomy that is based on metagenomic sequence data alone represents a substantial departure from the traditional reliance on phenotypic properties, the development of a robust framework for sequence-based virus taxonomy is indispensable for the comprehensive characterization of the global virome. In this Consensus Statement article, we consider the rationale for why metagenomic sequence data should, and how it can, be incorporated into the ICTV taxonomy, and present proposals that have been endorsed by the Executive Committee of the ICTV.

Most current methods for detecting natural selection from DNA sequence data are limited in that they are either based on summary statistics or a composite likelihood, and as a consequence, do not make full use of the information available in DNA sequence data. We here present a new importance sampling approach for approximating the full likelihood function for the selection coefficient. Our method CLUES treats the ancestral recombination graph (ARG) as a latent variable that is integrated out using previously published Markov Chain Monte Carlo (MCMC) methods. The method can be used for detecting selection, estimating selection coefficients, testing models of changes in the strength of selection, estimating the time of the start of a selective sweep, and for inferring the allele frequency trajectory of a selected or neutral allele. We perform extensive simulations to evaluate the method and show that it uniformly improves power to detect selection compared to current popular methods such as nSL and SDS, and can provide reliable inferences of allele frequency trajectories under many conditions. We also explore the potential of our method to detect extremely recent changes in the strength of selection. We use the method to infer the past allele frequency trajectory for a lactase persistence SNP (MCM6) in Europeans. We also infer the trajectory of a SNP (EDAR) in Han Chinese, finding evidence that this allele's age is much older than previously claimed. We also study a set of 11 pigmentation-associated variants. Several genes show evidence of strong selection particularly within the last 5,000 years, including ASIP, KITLG, and TYR. However, selection on OCA2/HERC2 seems to be much older and, in contrast to previous claims, we find no evidence of selection on TYRP1.

High-throughput sequencing technologies have strongly impacted microbiology, providing a rapid and cost-effective way of generating draft genomes and exploring microbial diversity. However, sequences obtained from impure nucleic acid preparations may contain DNA from sources other than the sample. Those sequence contaminations are a serious concern to the quality of the data used for downstream analysis, causing misassembly of sequence contigs and erroneous conclusions. Therefore, the removal of sequence contaminants is a necessary and required step for all sequencing projects. We developed DeconSeq, a robust framework for the rapid, automated identification and removal of sequence contamination in longer-read datasets (150 bp mean read length). DeconSeq is publicly available as standalone and web-based versions. The results can be exported for subsequent analysis, and the databases used for the web-based version are automatically updated on a regular basis. DeconSeq categorizes possible contamination sequences, eliminates redundant hits with higher similarity to non-contaminant genomes, and provides graphical visualizations of the alignment results and classifications. Using DeconSeq, we conducted an analysis of possible human DNA contamination in 202 previously published microbial and viral metagenomes and found possible contamination in 145 (72%) metagenomes with as high as 64% contaminating sequences. This new framework allows scientists to automatically detect and efficiently remove unwanted sequence contamination from their datasets while eliminating critical limitations of current methods. DeconSeq's web interface is simple and user-friendly. The standalone version allows offline analysis and integration into existing data processing pipelines. DeconSeq's results reveal whether the sequencing experiment has succeeded, whether the correct sample was sequenced, and whether the sample contains any sequence contamination from DNA preparation or host. In addition, the analysis of 202 metagenomes demonstrated significant contamination of the non-human associated metagenomes, suggesting that this method is appropriate for screening all metagenomes. DeconSeq is available at http://deconseq.sourceforge.net/.

Virus genome sequences, generated in ever-higher volumes, can provide new scientific insights and inform our responses to epidemics and outbreaks. To facilitate interpretation, such data must be organised and processed within scalable computing resources that encapsulate virology expertise. GLUE (Genes Linked by Underlying Evolution) is a data-centric bioinformatics environment for building such resources. The GLUE core data schema organises sequence data along evolutionary lines, capturing not only nucleotide data but associated items such as alignments, genotype definitions, genome annotations and motifs. Its flexible design emphasises applicability to different viruses and to diverse needs within research, clinical or public health contexts. HCV-GLUE is a case study GLUE resource for hepatitis C virus (HCV). It includes an interactive public web application providing sequence analysis in the form of a maximum-likelihood-based genotyping method, antiviral resistance detection and graphical sequence visualisation. HCV sequence data from GenBank is categorised and stored in a large-scale sequence alignment which is accessible via web-based queries. Whereas this web resource provides a range of basic functionality, the underlying GLUE project can also be downloaded and extended by bioinformaticians addressing more advanced questions. GLUE can be used to rapidly develop virus sequence data resources with public health, research and clinical applications. This streamlined approach, with its focus on reuse, will help realise the full value of virus sequence data.

The nucleotide sequences of plasmids pRC12 (12,342 bp; GC 43.99%) and pRC18 (18,664 bp; GC 34.33%), harbored by the bacteriocin-producer Lactobacillus curvatus CRL 705, were determined and analyzed. Plasmids pRC12 and pRC18 share a region with high DNA identity (> 83% identity between RepA, a Type II toxin-antitoxin system and a tyrosine integrase genes) and are stably maintained in their natural host L. curvatus CRL 705. Both plasmids are low copy number and belong to the theta-type replicating group. While pRC12 is a pUCL287-like plasmid that possesses iterons and the repA and repB genes for replication, pRC18 harbors a 168 amino acid replication protein affiliated to RepB, which was named RepB'. Plasmid pRC18 also possesses a pUCL287-like repA gene but it was disrupted by an 11 kb insertion element that contains RepB', several transposases/IS elements, and the lactocin Lac705 operon. An Escherichia coli / Lactobacillus shuttle vector, named plasmid p3B1, carrying the pRC18 replicon (i.e. repB' and replication origin), a chloramphenicol resistance gene and a pBluescript backbone, was constructed and used to define the host range of RepB'. Chloramphenicol-resistant transformants were obtained after electroporation of Lactobacillus plantarum CRL 691, Lactobacillus sakei 23K and a plasmid-cured derivative of L. curvatus CRL 705, but not of L. curvatus DSM 20019 or Lactococcus lactis NZ9000. Depending on the host, transformation efficiency ranged from 102 to 107 per μg of DNA; in the new hosts, the plasmid was relatively stable as 29-53% of recombinants kept it after cell growth for 100 generations in the absence of selective pressure. Plasmid p3B1 could therefore be used for cloning and functional studies in several Lactobacillus species.

It has recently been demonstrated that the nucleobase-density profiles of mRNA coding sequences are related in a complementary manner to the nucleobase-affinity profiles of their cognate protein sequences. Based on this, it has been proposed that cognate mRNA/protein pairs may bind in a co-aligned manner, especially if unstructured. Here, we study the dependence of mRNA/protein sequence complementarity on the properties of the nucleobase/amino-acid affinity scales used. Specifically, we sample the space of randomly generated scales by employing a Monte Carlo strategy with a fitness function that depends directly on the level of complementarity. For model organisms representing all three domains of life, we show that even short searches reproducibly converge upon highly optimized scales, implying that the topology of the underlying fitness landscape is decidedly funnel-like. Furthermore, the optimized scales, generated without any consideration of the physicochemical attributes of nucleobases or amino acids, resemble closely the nucleobase/amino-acid binding affinity scales obtained from experimental structures of RNA-protein complexes. This provides support for the claim that mRNA/protein sequence complementarity may indeed be related to binding between the two. Finally, we characterize suboptimal scales and show that intermediate-to-high complementarity can be reached by substantially diverse scales, but with select amino acids contributing disproportionally. Our results expose the dependence of cognate mRNA/protein sequence complementarity on the properties of the underlying nucleobase/amino-acid affinity scales and provide quantitative constraints that any physical scales need to satisfy for the complementarity to hold.

Background/aims: Juvenile polyposis syndrome (JPS) is a rare autosomal dominant disorder characterised by multiple gastrointestinal juvenile polyps and an increased risk of colorectal cancer. This syndrome is caused by germline mutation of either SMAD4 or BMPR1A, and possibly ENG. PTEN, originally linked to Cowden syndrome and Bannayan–Riley–Ruvalcaba syndrome, has also been associated with JPS. By direct sequencing, germline mutations are found in only 30–40% of patients with a JPS phenotype. Therefore, alternative ways of inactivation of the known JPS genes, or additional genes predisposing to JPS may be involved. In this study, a comprehensive genetic analysis of SMAD4, BMPR1A, PTEN and ENG is performed through direct sequencing and multiplex ligation-dependent probe amplification (MLPA) in JPS patients. Methods: Archival material of 29 patients with JPS from 27 families was collected. Direct sequencing and MLPA analysis were performed to search for germline defects in SMAD4, BMPR1A, PTEN and ENG. Results: A germline defect in SMAD4, BMPR1A or PTEN was found in 13 of 27 (48.1%) unrelated JPS patients. Nine mutations (33.3%) were detected by direct sequencing, including six (22.2%) SMAD4 mutations and three (11.1%) BMPR1A mutations. MLPA identified four additional patients (14.8%) with germline hemizygous large genomic deletions, including one deletion of SMAD4, one deletion of exons 10 and 11 of BMPR1A, and two unrelated patients with deletion of both BMPR1A and PTEN. No ENG gene mutations were found. Conclusion: Large genomic deletions of SMAD4, BMPR1A and PTEN are a common cause of JPS. Using direct sequencing and MLPA, a germline defect was detected in 48.1% of JPS patients. MLPA identified 14.8% (4/27) of these mutations. Since a substantial percentage of JPS patients carry a germline deletion and MLPA is a reliable and user-friendly technique, it is concluded that MLPA is a valuable adjunct in JPS diagnosis.

Tuberculosis is an infectious bacterial disease caused by Mycobacterium tuberculosis. It remains a major health threat, killing over one million people every year worldwide. An early antibiotic therapy is the basis of the treatment, and the emergence and spread of multidrug and extensively drug-resistant mutant strains raise significant challenges. As these bacteria grow very slowly, drug resistance mutations are currently detected using molecular biology techniques. Resistance mutations are identified by sequencing the resistance-linked genes followed by a comparison with the literature data. The only online database is the TB Drug Resistance Mutation database (TBDReaM database); however, it requires mutation detection before use, and its interrogation is complex due to its loose syntax and grammar. The MUBII-TB-DB database is a simple, highly structured text-based database that contains a set of Mycobacterium tuberculosis mutations (DNA and proteins) occurring at seven loci: rpoB, pncA, katG; mabA(fabG1)-inhA, gyrA, gyrB, and rrs. Resistance mutation data were extracted after the systematic review of MEDLINE referenced publications before March 2013. MUBII analyzes the query sequence obtained by PCR-sequencing using two parallel strategies: i) a BLAST search against a set of previously reconstructed mutated sequences and ii) the alignment of the query sequences (DNA and its protein translation) with the wild-type sequences. The post-treatment includes the extraction of the aligned sequences together with their descriptors (position and nature of mutations). The whole procedure is performed using the internet. The results are graphs (alignments) and text (description of the mutation, therapeutic significance). The system is quick and easy to use, even for technicians without bioinformatics training. MUBII-TB-DB is a structured database of the mutations occurring at seven loci of major therapeutic value in tuberculosis management. Moreover, the system provides interpretation of the mutations in biological and therapeutic terms and can evolve by the addition of newly described mutations. Its goal is to provide easy and comprehensive access through a client-server model over the Web to an up-to-date database of mutations that lead to the resistance of M. tuberculosis to antibiotics.