Short clones or long clones? A simulation study on the use of paired reads in metagenomics

<p>Abstract</p> <p>Background</p> <p>Metagenomics is the study of environmental samples using sequencing. Rapid advances in sequencing technology are fueling a vast increase in the number and scope of metagenomics projects. Most metagenome sequencing projects so far have been based on Sanger or Roche-454 sequencing, as only these technologies provide long enough reads, while Illumina sequencing has not been considered suitable for metagenomic studies due to a short read length of only 35 bp. However, now that reads of length 75 bp can be sequenced in pairs, Illumina sequencing has become a viable option for metagenome studies.</p> <p>Results</p> <p>This paper addresses the problem of taxonomical analysis of paired reads. We describe a new feature of our metagenome analysis software MEGAN that allows one to process sequencing reads in pairs and makes assignments of such reads based on the combined bit scores of their matches to reference sequences. Using this new software in a simulation study, we investigate the use of Illumina paired-sequencing in taxonomical analysis and compare the performance of single reads, short clones and long clones. In addition, we also compare against simulated Roche-454 sequencing runs.</p> <p>Conclusion</p> <p>This work shows that paired reads perform better than single reads, as expected, but also, perhaps slightly less obviously, that long clones allow more specific assignments than short ones. A new version of the program MEGAN that explicitly takes paired reads into account is available from our website.</p

Regulatory T Cells in the Pathogenesis and Healing of Chronic Human Dermal Leishmaniasis Caused by Leishmania (Viannia) Species

The immune inflammatory response is a double edged sword. During infectious diseases, regulatory T cells can prevent eradication of the pathogen but can also limit inflammation and tissue damage. We investigated the role of regulatory T cells in chronic dermal leishmaniasis caused by species of the parasite Leishmania that are endemic in South and Central America. We found that although individuals with chronic lesions have increased regulatory T cells in their blood and at skin sites where immune responses to Leishmania were taking place compared to infected individuals who do not develop disease, their capacity to control the inflammatory response to Leishmania was inferior. However, healing of chronic lesions at the end of treatment was accompanied by an increase in the number and capacity of regulatory T cells to inhibit the function of effector T cells that mediate the inflammatory response. Different subsets of regulatory T cells, defined by the expression of molecular markers, were identified during chronic disease and healing, supporting the participation of distinct regulatory T cells in the development of disease and the control of inflammation during the healing response. Immunotherapeutic strategies may allow these regulatory T cell subsets to be mobilized or mitigated to achieve healing

A Whole-Genome Analysis Framework for Effective Identification of Pathogenic Regulatory Variants in Mendelian Disease

The interpretation of non-coding variants still constitutes a major challenge in the application of whole-genome sequencing in Mendelian disease, especially for single-nucleotide and other small non-coding variants. Here we present Genomiser, an analysis framework that is able not only to score the relevance of variation in the non-coding genome, but also to associate regulatory variants to specific Mendelian diseases. Genomiser scores variants through either existing methods such as CADD or a bespoke machine learning method and combines these with allele frequency, regulatory sequences, chromosomal topological domains, and phenotypic relevance to\ua0discover variants associated to specific Mendelian disorders. Overall, Genomiser is able to identify causal regulatory variants as the\ua0top candidate in 77% of simulated whole genomes, allowing effective detection and discovery of regulatory variants in Mendelian disease

PEDIA: prioritization of exome data by image analysis

Purpose Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. Methods Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. Results The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene. Conclusion Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

BACKGROUND: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data was donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups