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Background: For many years, a major question in the field of cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, which are often composed of multiple DNA breaks, resulting in difficulties to classify and interpret them functionally. Despite recent efforts towards the classification of structural variants (SVs), more robust statistical frames are needed to better classify these variants, and to isolate those that derive from specific molecular mechanisms. Results: We present a new statistical approach to analyze SVs patterns from 2392 real tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and to identify significant recurrence, which can inform of relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified. Conclusions: We developed a new and unbiased methodology for clustering SVs to search further for complex patterns by using a cost-efficient graph mining method. Followed by deep statistical analysis and applying randomization techniques, our proposed framework allows for discerning between stochastic chromosomal rearrangements and complex patterns that might have specific underlying mechanisms present in different cancer types.

Background The taxonomic and phylogenetic classification based on sequence analysis of the ITS1 genomic region has become a crucial component of fungal ecology and diversity studies. Nowadays, there is no accurate alignment-free classification tool for fungal ITS1 sequences for large environmental surveys. This study describes the development of a machine learning-based classifier for the taxonomical assignment of fungal ITS1 sequences at the genus level. Results A fungal ITS1 sequence database was built using curated data. Training and test sets were generated from it. A Naïve Bayesian classifier was built using features from the primary sequence with an accuracy of 87 % in the classification at the genus level. Conclusions The final model was based on a Naïve Bayes algorithm using ITS1 sequences from 510 fungal genera. This classifier, denoted as Mycofier, provides similar classification accuracy compared to BLASTN, but the database used for the classification contains curated data and the tool, independent of alignment, is more efficient and contributes to the field, given the lack of an accurate classification tool for large data from fungal ITS1 sequences. The software and source code for Mycofier are freely available at https://github.com/ldelgado-serrano/mycofier.git. Electronic supplementary material The online version of this article (doi:10.1186/s13104-016-2203-3) contains supplementary material, which is available to authorized users.

During the last decade, there has been increasing awareness of the massive number of microorganisms, collectively known as the human microbiota, that are associated with humans. This microbiota outnumbers the host cells by approximately a factor of ten and contains a large repertoire of microbial genome-encoded metabolic processes. The diverse human microbiota and its associated metabolic potential can provide the host with novel functions that can influence host health and disease status in ways that still need to be analyzed. The microbiota varies with age, with features that depend on the body site, host lifestyle and health status. The challenge is therefore to identify and characterize these microbial communities and use this information to learn how they function and how they can influence the host in terms of health and well-being. Here we provide an overview of some of the recent studies involving the human microbiota and about how these communities might affect host health and disease. A special emphasis is given to studies related to tuberculosis, a disease that claims over one million lives each year worldwide and still represents a challenge for control in many countries, including Colombia. En las últimas décadas ha incrementado nuestro conocimiento sobre la gran cantidad de microorganismos que conviven con nosotros, comunidades que colectivamente se conocen como la microbiota humana. El número de microorganismos que conforman la microbiota supera el número de células del cuerpo humano por un factor de diez aproximadamente y aporta un gran repertorio de genes y procesos metabólicos. La diversidad de la microbiota humana y su potencial metabólico brindan al hospedero una serie de funciones que complementan sus procesos y a su vez pueden influir sobre la salud del ser humano en formas que apenas se empiezan a conocer. La microbiota varía desde el nacimiento hasta la vejez del individuo, con características que dependen del sitio corporal, del estilo de vida y del estado de salud del hospedero. El reto actual es aprovechar el conocimiento derivado de la identificación y caracterización de estas comunidades microbianas para entender cómo funcionan estos microorganismos y cómo pueden influir de forma positiva o negativa sobre la salud del humano. En este documento ofrecemos una revisión general de algunos estudios recientes sobre la microbiota humana y su posible efecto en el hospedero en términos de salud y bienestar. Igualmente, se mencionan estudios sobre microbiota y su posible asociación con la tuberculosis, una enfermedad que todavía cobra más de un millón de vidas anualmente a nivel mundial y cuyo control todavía representa un gran reto en varios países del mundo, incluido Colombia.

The microbial community of a Colombian high mountain hot spring, El Coquito, was analyzed using three different culture-independent assessments of 16S ribosomal RNA genes: clone libraries, pyrosequencing of the V5-V6 hypervariable region, and microarray. This acidic spring had a diverse community composed mainly of Bacteria that shared characteristics with those from other hot springs and extreme acidic environments. The microbial community was dominated by Proteobacteria, Firmicutes, and Planctomycetes and contained chemotrophic bacteria potentially involved in cycling of ferrous and sulfur-containing minerals and phototrophic organisms, most of which were eukaryotic micro-algae. Despite the presence of a large proportion of novel, unclassified sequences, the taxonomic profiles obtained with each strategy showed similarities at higher taxonomic levels. However, some groups, such as Spirochaetes and Aquificae, were identified using only one methodology, and more taxa were detected with the gene array, which also shared more groups with the pyrosequencing data. Overall, the combined use of different approaches provided a broader view of the microbial community in this acidic hot spring.

Background Changes in respiratory tract microbiota have been associated with diseases such as tuberculosis, a global public health problem that affects millions of people each year. This pilot study was carried out using sputum, oropharynx, and nasal respiratory tract samples collected from patients with pulmonary tuberculosis and healthy control individuals, in order to compare sample types and their usefulness in assessing changes in bacterial and fungal communities. Findings Most V1-V2 16S rRNA gene sequences belonged to the phyla Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, and Fusobacteria, with differences in relative abundances and in specific taxa associated with each sample type. Most fungal ITS1 sequences were classified as Ascomycota and Basidiomycota, but abundances differed for the different samples. Bacterial and fungal community structures in oropharynx and sputum samples were similar to one another, as indicated by several beta diversity analyses, and both differed from nasal samples. The only difference between patient and control microbiota was found in oropharynx samples for both bacteria and fungi. Bacterial diversity was greater in sputum samples, while fungal diversity was greater in nasal samples. Conclusions Respiratory tract microbial communities were similar in terms of the major phyla identified, yet they varied in terms of relative abundances and diversity indexes. Oropharynx communities varied with respect to health status and resembled those in sputum samples, which are collected from tuberculosis patients only due to the difficulty in obtaining sputum from healthy individuals, suggesting that oropharynx samples can be used to analyze community structure alterations associated with tuberculosis.

Microbial explorations of hot springs have led to remarkable discoveries and improved our understanding of life under extreme conditions. The Andean Mountains harbor diverse habitats, including an extensive chain of geothermal heated water sources. In this study, we describe and compare the planktonic microbial communities present in five high-mountain hot springs with distinct geochemical characteristics, at varying altitudes and geographical locations in the Colombian Andes. The diversity and structure of the microbial communities were assessed by pyrosequencing the V5 - V6 region of the 16S rRNA gene. The planktonic communities varied in terms of diversity indexes and were dominated by the bacterial phyla Proteobacteria, Aquificae, Chloroflexi, Cyanobacteria, Firmicutes, Nitrospirae, and Thermotogae, with site-specific bacterial taxa also observed in some cases. Statistical analyses showed that these microbial communities were distinct from one another and that they clustered in a manner consistent with physicochemical parameters of the environment sampled. Multivariate analysis suggested that pH and sulfate were among the main variables influencing population structure and diversity. The results show that despite their geographical proximity and some shared geochemical characteristics, there were few shared operational taxonomic units (OTUs) and that community structure was influenced mainly by environmental factors that have resulted in different microbial populations.