KOMB: K-core based de novo characterization of copy number variation in microbiomes.

Characterizing metagenomes via kmer-based, database-dependent taxonomic classification has yielded key insights into underlying microbiome dynamics. However, novel approaches are needed to track community dynamics and genomic flux within metagenomes, particularly in response to perturbations. We describe KOMB, a novel method for tracking genome level dynamics within microbiomes. KOMB utilizes K-core decomposition to identify Structural variations (SVs), specifically, population-level Copy Number Variation (CNV) within microbiomes. K-core decomposition partitions the graph into shells containing nodes of induced degree at least K, yielding reduced computational complexity compared to prior approaches. Through validation on a synthetic community, we show that KOMB recovers and profiles repetitive genomic regions in the sample. KOMB is shown to identify functionally-important regions in Human Microbiome Project datasets, and was used to analyze longitudinal data and identify keystone taxa in Fecal Microbiota Transplantation (FMT) samples. In summary, KOMB represents a novel graph-based, taxonomy-oblivious, and reference-free approach for tracking CNV within microbiomes. KOMB is open source and available for download at https://gitlab.com/treangenlab/komb.

Avances de la bioinformática en Costa Rica: vista retrospectiva y perspectivas / Bioinformatics advances in Costa Rica: retrospective view and perspectives

Resumen Introducción: La disciplina científica de la bioinformática tiene el potencial de generar aplicaciones innovadoras para las sociedades humanas. Costa Rica, pequeña en tamaño y población en comparación con otros países de América Latina, ha ido adoptando la disciplina de manera progresiva. El reconocer los avances permite determinar hacia dónde puede dirigirse el país en este campo, así como su contribución a la región latinoamericana. Objetivo: En este manuscrito se reporta evidencia de la evolución de la bioinformática en Costa Rica, para identificar debilidades y fortalezas que permitan definir acciones a futuro. Métodos: Se realizaron búsquedas en bases de datos de publicaciones científicas y repositorios de secuencias, así como información de actividades de capacitación, redes, infraestructura, páginas web y fuentes de financiamiento. Resultados: Se observan avances importantes desde el 2010, incluyendo un aumento en oportunidades de entrenamiento y número de publicaciones, aportes significativos a las bases de datos de secuencias y conexiones por medio de redes. Sin embargo, ciertas áreas, como la masa crítica y la financiación requieren más desarrollo. La comunidad científica y sus patrocinadores deben promover la investigación basada en bioinformática, invertir en la formación de estudiantes de posgrado, aumentar la formación de profesionales, crear oportunidades laborales para carreras en bioinformática y promover colaboraciones internacionales a través de redes. Conclusiones: Se sugiere que para experimentar los beneficios de las aplicaciones de la bioinformática se deben fortalecer tres aspectos clave: la comunidad científica, la infraestructura de investigación y las oportunidades de financiamiento. El impacto de tal inversión sería el desarrollo de proyectos ambiciosos pero factibles y colaboraciones extendidas dentro de la región latinoamericana. Esto permitiría realizar contribuciones significativas para abordar los desafíos globales y la aplicación de nuevos enfoques de investigación, innovación y transferencia de conocimiento para el desarrollo de la economía, dentro de un marco de ética de la investigación.

Abstract Introduction: The scientific discipline of bioinformatics has the potential to generate innovative applications for human societies. Costa Rica, small in size and population compared to other Latin American countries, has been progressively adopting the discipline. Recognizing progress makes it possible to determine where the country can go in this field, as well as its contribution to the Latin American region. Objective: This manuscript reports evidence of the evolution of bioinformatics in Costa Rica, to identify weaknesses and strengths allowing future actions plans. Methods: We searched databases of scientific publications and sequence repositories, as well as information on training activities, networks, infrastructure, web pages and funding sources. Results: Important advances have been observed since 2010, such as increases in training opportunities and the number of publications, significant contributions to the sequence databases and connections through networks. However, areas such as critical mass and financing require further development. The scientific community and its sponsors should promote bioinformatics-based research, invest in graduate student training, increase professional training, create career opportunities in bioinformatics, and promote international collaborations through networks. Conclusions: It is suggested that in order to experience the benefits of bioinformatics applications, three key aspects must be strengthened: the scientific community, the research infrastructure, and funding opportunities. The impact of such investment would be the development of ambitious but feasible projects and extended collaborations within the Latin American region and abroad. This would allow significant contributions to address global challenges and the implementation of new approaches to research, innovation and knowledge transfer for the development of the economy, within an ethics of research framework.

A Comprehensive Study of De Novo Genome Assemblers: Current Challenges and Future Prospective.

BACKGROUND: Current advancements in next-generation sequencing technology have made possible to sequence whole genome but assembling a large number of short sequence reads is still a big challenge. In this article, we present the comparative study of seven assemblers, namely, ABySS, Velvet, Edena, SGA, Ray, SSAKE, and Perga, using prokaryotic and eukaryotic paired-end as well as single-end data sets from Illumina platform. RESULTS: Results showed that in case of single-end data sets, Velvet and ABySS outperformed in all the seven assemblers with comparatively low assembling time and high genome fraction. Velvet consumed the least amount of memory than any other assembler. In case of paired-end data sets, Velvet consumed least amount of time and produced high genome fraction after ABySS and Ray. In terms of low memory usage, SGA and Edena outperformed in all the assemblers. Ray also showed good genome fraction; however, extremely high assembling time consumed by the Ray might make it prohibitively slow on larger data sets of single and paired-end data. CONCLUSIONS: Our comparison study will provide assistance to the scientists for selecting the suitable assembler according to their data sets and will also assist the developers to upgrade or develop a new assembler for de novo assembling.