Microbial communities are vital in the functioning of all ecosystems; however, most microorganisms are uncultivated, and their roles in natural systems are unclear. Here, using random shotgun sequencing of DNA from a natural acidophilic biofilm, we report reconstruction of near-complete genomes of Leptospirillum group II and Ferroplasma type II, and partial recovery of three other genomes. This was possible because the biofilm was dominated by a small number of species populations and the frequency of genomic rearrangements and gene insertions or deletions was relatively low. Because each sequence read came from a different individual, we could determine that single-nucleotide polymorphisms are the predominant form of heterogeneity at the strain level. The Leptospirillum group II genome had remarkably few nucleotide polymorphisms, despite the existence of low-abundance variants. The Ferroplasma type II genome seems to be a composite from three ancestral strains that have undergone homologous recombination to form a large population of mosaic genomes. Analysis of the gene complement for each organism revealed the pathways for carbon and nitrogen fixation and energy generation, and provided insights into survival strategies in an extreme environment.

http://www.envismadrasuniv.org/pdf/microbial%20genome%20environment.pdf

Community structure and metabolism through reconstruction of microbial genomes from the environment

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2017.0387

Opportunities and obstacles for deep learning in biology and medicine.

Microbial communities are critical to ecosystem function. A key objective of metagenomic studies is to analyse organism-specific metabolic pathways and reconstruct community interaction networks. This requires accurate assignment of assembled genome fragments to genomes. Existing binning methods often fail to reconstruct a reasonable number of genomes and report many bins of low quality and completeness. Furthermore, the performance of existing algorithms varies between samples and biotopes. Here, we present a dereplication, aggregation and scoring strategy, DAS Tool, that combines the strengths of a flexible set of established binning algorithms. DAS Tool applied to a constructed community generated more accurate bins than any automated method. Indeed, when applied to environmental and host-associated samples of different complexity, DAS Tool recovered substantially more near-complete genomes, including previously unreported lineages, than any single binning method alone. The ability to reconstruct many near-complete genomes from metagenomics data will greatly advance genome-centric analyses of ecosystems.

/pdf/recovery-of-genomes-from-metagenomes-via-a-dereplication-p46rhshwuu.pdf

Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy.

Recombination is typically thought of as a symmetrical process resulting in large-scale reciprocal genetic exchanges between homologous chromosomes. Recombination events, however, are also accompanied by short-scale, unidirectional exchanges known as gene conversion in the neighborhood of the initiating double-strand break. A large body of evidence suggests that gene conversion is GC-biased in many eukaryotes, including mammals and human. AT/GC heterozygotes produce more GC- than AT-gametes, thus conferring a population advantage to GC-alleles in high-recombining regions. This apparently unimportant feature of our molecular machinery has major evolutionary consequences. Structurally, GC-biased gene conversion explains the spatial distribution of GC-content in mammalian genomes—the so-called isochore structure. Functionally, GC-biased gene conversion promotes the segregation and fixation of deleterious AT → GC mutations, thus increasing our genomic mutation load. Here we review the recent evidence for a GC...

/pdf/biased-gene-conversion-and-the-evolution-of-mammalian-17ih1l2zfd.pdf

Biased gene conversion and the evolution of mammalian genomic landscapes.

Metabolomics now plays a significant role in fundamental plant biology and applied biotechnology. Plants collectively produce a huge array of chemicals, far more than are produced by most other organisms; hence, metabolomics is of great importance in plant biology. Although substantial improvements have been made in the field of metabolomics, the uniform annotation of metabolite signals in databases and informatics through international standardization efforts remains a challenge, as does the development of new fields such as fluxome analysis and single cell analysis. The principle of transcript and metabolite cooccurrence, particularly transcriptome coexpression network analysis, is a powerful tool for decoding the function of genes in Arabidopsis thaliana. This strategy can now be used for the identification of genes involved in specific pathways in crops and medicinal plants. Metabolomics has gained importance in biotechnology applications, as exemplified by quantitative loci analysis, prediction of food quality, and evaluation of genetically modified crops. Systems biology driven by metabolome data will aid in deciphering the secrets of plant cell systems and their application to biotechnology.

Metabolomics for Functional Genomics, Systems Biology, and Biotechnology

With the increasing amount of available genome sequences, novel tools are needed for comprehensive analysis of species-specific sequence characteristics for a wide variety of genomes. We used an unsupervised neural network algorithm, a self-organizing map (SOM), to analyze di-, tri-, and tetranucleotide frequencies in a wide variety of prokaryotic and eukaryotic genomes. The SOM, which can cluster complex data efficiently, was shown to be an excellent tool for analyzing global characteristics of genome sequences and for revealing key combinations of oligonucleotides representing individual genomes. From analysis of 1- and 10-kb genomic sequences derived from 65 bacteria (a total of 170 Mb) and from 6 eukaryotes (460 Mb), clear species-specific separations of major portions of the sequences were obtained with the di-, tri-, and tetranucleotide SOMs. The unsupervised algorithm could recognize, in most 10-kb sequences, the species-specific characteristics (key combinations of oligonucleotide frequencies) that are signature features of each genome. We were able to classify DNA sequences within one and between many species into subgroups that corresponded generally to biological categories. Because the classification power is very high, the SOM is an efficient and fundamental bioinformatic strategy for extracting a wide range of genomic information from a vast amount of sequences.

Informatics for Unveiling Hidden Genome Signatures

With the increasing amount of available genome sequences, novel tools are needed for comprehensive analysis of species-specific sequence characteristics for a wide variety of genomes. We used an unsupervised neural network algorithm, Kohonen’s self-organizing map (SOM), to analyze diand trinucleotide frequencies in 9 eukaryotic genomes of known sequences (a total of 1.2 Gb); S. cerevisiae, S. pombe, C. elegans, A. thaliana, D. melanogaster, Fugu, and rice, as well as P. falciparum chromosomes 2 and 3, and human chromosomes 14, 20, 21, and 22, that have been almost completely sequenced. Each genomic sequence with different window sizes was encoded as a 16and 64-dimensional vector giving relative frequencies of di- and trinucleotides, respectively. From analysis of a total of 120,000 nonoverlapping 10-kb sequences and overlapping 100-kb sequences with a moving step size of 10 kb, derived from a total of the 1.2 Gb genomic sequences, clear species-specific separations of most sequences were obtained with the SOMs. The unsupervised algorithm could recognize, in most of the 120,000 10-kb sequences, the species-specific characteristics (key combinations of oligonucleotide frequencies) that are signature representations of each genome. Because the classification power is very high, the SOMs can provide fundamental bioinformatic strategies for extracting a wide range of genomic information that could not otherwise be obtained.

A novel bioinformatic strategy for unveiling hidden genome signatures of eukaryotes: self-organizing map of oligonucleotide frequency.

The completion of the human genome sequence will greatly accelerate development of a new branch of bioscience and provide fundamental knowledge to biomedical research. We used the sequence information to measure replication timing of the entire lengths of human chromosomes 11q and 21q. Megabase-sized zones that replicate early or late in S phase (thus early/late transition) were defined at the sequence level. Early zones were more GC-rich and gene-rich than were late zones, and early/late transitions occurred primarily at positions identical to or near GC% transitions. We also found the single nucleotide polymorphism (SNP) frequency was high in the late-replicating and replication-transition regions. In the early/late transition regions, concentrated occurrence of cancer-related genes that include CCND1 encoding cyclin D1 (BCL1), FGF4 (KFGF), TIAM1 and FLI1, was observed. The transition regions contained other disease-related genes including APP associated with familial Alzheimer's disease (AD1), SOD1 associated with familial amyotrophic lateral sclerosis (ALS1) and PTS associated with phenylketonuria. These findings are discussed with respect to the prediction that increased DNA damage occurs in replication-transition regions. We propose that genome-wide assessment of replication timing serves as an efficient strategy for identifying disease-related genes.

/pdf/chromosome-wide-assessment-of-replication-timing-for-human-4zhpqji7o0.pdf

Chromosome-wide assessment of replication timing for human chromosomes 11q and 21q: disease-related genes in timing-switch regions

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Informatics for Unveiling Hidden Genome Signatures

A novel bioinformatic strategy for unveiling hidden genome signatures of eukaryotes: self-organizing map of oligonucleotide frequency.

Chromosome-wide assessment of replication timing for human chromosomes 11q and 21q: disease-related genes in timing-switch regions