PyCogent: a toolkit for making sense from sequence

Illumina MiSeq 16S rRNA Gene Library Preparation for Poultry Processing Microbiome Analyses

The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, *a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety.This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.

Characterizing the diversity and commensal origins of penA mosaicism in the genus Neisseria

Mosaic penA alleles formed through horizontal gene transfer (HGT) have been instrumental to the rising incidence of ceftriaxone-resistant gonococcal infections. Although interspecies HGT of regions of the penA gene between Neisseria gonorrhoeae and commensal Neisseria species has been described, knowledge concerning which species are the most common contributors to mosaic penA alleles is limited, with most studies examining only a small number of alleles. Here, we investigated the origins of recombinant penA alleles through in silico analyses that incorporated 1700 penA alleles from 35 513 Neisseria isolates, comprising 15 different Neisseria species. We identified Neisseria subflava and Neisseria cinerea as the most common source of recombinant sequences in N. gonorrhoeae penA. This contrasted with Neisseria meningitidis penA, for which the primary source of recombinant DNA was other meningococci, followed by Neisseria lactamica. Additionally, we described the distribution of polymorphisms implicated in antimicrobial resistance in penA, and found that these are present across the genus. These results provide insight into resistance-related changes in the penA gene across human-associated Neisseria species, illustrating the importance of genomic surveillance of not only the pathogenic Neisseria, but also of the oral niche-associated commensals from which these pathogens are sourcing key genetic variation.

Roadmap to the study of gene and protein phylogeny and evolution-A practical guide

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

Mining metatranscriptomes reveals a vast world of viroid-like circular RNAs

Viroids and viroid-like covalently closed circular (ccc) RNAs are minimal replicators that typically encode no proteins and hijack cellular enzymes for replication. The extent and diversity of viroid-like agents are poorly understood. We developed a computational pipeline to identify viroid-like cccRNAs and applied it to 5,131 metatranscriptomes and 1,344 plant transcriptomes. The search yielded 11,378 viroid-like cccRNAs spanning 4,409 species-level clusters, a 5-fold increase compared to the previously identified viroid-like elements. Within this diverse collection, we discovered numerous putative viroids, satellite RNAs, retrozymes, and ribozy-like viruses. Diverse ribozyme combinations and unusual ribozymes within the cccRNAs were identified. Self-cleaving ribozymes were identified in ambiviruses, some mito-like viruses and capsid-encoding satellite virus-like cccRNAs. The broad presence of viroid-like cccRNAs in diverse transcriptomes and ecosystems implies that their host range is far broader than currently known, and matches to CRISPR spacers suggest that some cccRNAs replicate in prokaryotes.

Bacterial community dynamics explain carbon mineralization and assimilation in soils of different land-use history

Soil dwelling microorganisms are key players in the terrestrial carbon cycle, driving both the degradation and stabilization of soil organic matter. Bacterial community structure and function vary with respect to land-use, yet the ecological drivers of this variation remain poorly described and difficult to predict. We conducted a multi-substrate DNA-stable isotope probing experiment across cropland, old-field, and forest habitats to link carbon mineralization dynamics with the dynamics of bacterial growth and carbon assimilation. We tracked the movement of ¹³ C derived from five distinct carbon sources as it was assimilated into bacterial DNA over time. We show that carbon mineralization, community composition, and carbon assimilation dynamics all differed with respect to land-use. We also show that microbial community dynamics affect carbon assimilation dynamics and are associated with soil DNA content. Soil DNA yield is easy to measure and may be useful in predicting microbial community dynamics linked to soil carbon cycling. This article is protected by copyright. All rights reserved.

Finding recurrent RNA structural networks with fast maximal common subgraphs of edge-colored graphs

RNA tertiary structure is crucial to its many non-coding molecular functions. RNA architecture is shaped by its secondary structure composed of stems, stacked canonical base pairs, enclosing loops. While stems are precisely captured by free-energy models, loops composed of non-canonical base pairs are not. Nor are distant interactions linking together those secondary structure elements (SSEs). Databases of conserved 3D geometries (a.k.a. modules) not captured by energetic models are leveraged for structure prediction and design, but the computational complexity has limited their study to local elements, loops. Representing the RNA structure as a graph has recently allowed to expend this work to pairs of SSEs, uncovering a hierarchical organization of these 3D modules, at great computational cost. Systematically capturing recurrent patterns on a large scale is a main challenge in the study of RNA structures. In this paper, we present an efficient algorithm to compute maximal isomorphisms in edge colored graphs. We extend this algorithm to a framework well suited to identify RNA modules, and fast enough to considerably generalize previous approaches. To exhibit the versatility of our framework, we first reproduce results identifying all common modules spanning more than 2 SSEs, in a few hours instead of weeks. The efficiency of our new algorithm is demonstrated by computing the maximal modules between any pair of entire RNA in the non-redundant corpus of known RNA 3D structures. We observe that the biggest modules our method uncovers compose large shared sub-structure spanning hundreds of nucleotides and base pairs between the ribosomes of Thermus thermophilus, Escherichia Coli, and Pseudomonas aeruginosa.

Ribonucleic Acids (RNAs) are performing a broad range of essential molecular functions in cells, many of which rely on intricate folding properties of the molecule. Watson-Crick and Wobble base pairs form early, stack onto each other to create stems connected by loops, which are themselves stabilized by more sophisticated base interaction patterns. These networks are essential to shape RNA 3D structures but unfortunately still poorly understood. Here, we undertake the task to build a catalog of base interaction networks occurring in multiple structures. However, a pairwise comparison of all RNA structures is computationally heavy. Therefore, we devise an algorithm leveraging intrinsic properties of RNA base interaction networks that enables us to quickly mine full databases of 3D structures. Compared to previous methods, our techniques bring the total running time of the analysis from months to hours while performing more general searches. The data collected though this work will benefit molecular evolution studies and serve in structure prediction tools.

Community Organization and Metagenomics of Bacterial Assemblages Across Local Scale pH Gradients in Northern Forest Soils

Soil pH has shown to predict bacterial diversity, but mechanisms are still poorly understood. To investigate how bacteria distribute themselves as a function of soil pH, we assessed community composition, diversity, assembly, and gene abundance across local (ca. 1 km) scale gradients in soil pH from ~ 3.8 to 6.5 created by differences in soil parent material in three northern forests. Plant species were the same on all sites, with no evidence of agriculture in the past. Concentrations of extractable calcium, iron, and phosphorus also varied significantly across the pH gradients. Among taxa, Alphaproteobacteria and Acidobacteria were more common in soils with acidic pH values. Overall richness and diversity of OTUs peaked at intermediate pH values. Variations in OTU richness and diversity also had a quadratic fit with concentrations of extractable calcium and phosphorus. Community assembly was via homogeneous deterministic processes in soils with acidic pH values, whereas stochastic processes dominated in soils with near-neutral pH values. Although we expected selection via genes for acid tolerance response in acidic soils, genes for genetic information processing were more selective. Taxa in higher pH soils had differential abundance of transporter genes, suggesting adaptation to acquire metabolic substrates from soils. Soil bacterial communities in northern forest soils are incredibly diverse, and we still have much to learn about how soil pH and co-varying soil parameters directly drive gene selection in this critical component of ecosystem structure.

EMPress Enables Tree-Guided, Interactive, and Exploratory Analyses of Multi-omic Data Sets

Standard workflows for analyzing microbiomes often include the creation and curation of phylogenetic trees. Here we present EMPress, an interactive web tool for visualizing trees in the context of microbiome, metabolome, and other community data scalable to trees with well over 500,000 nodes. EMPress provides novel functionality—including ordination integration and animations—alongside many standard tree visualization features and thus simplifies exploratory analyses of many forms of ‘omic data.

IMPORTANCE Phylogenetic trees are integral data structures for the analysis of microbial communities. Recent work has also shown the utility of trees constructed from certain metabolomic data sets, further highlighting their importance in microbiome research. The ever-growing scale of modern microbiome surveys has led to numerous challenges in visualizing these data. In this paper we used five diverse data sets to showcase the versatility and scalability of EMPress, an interactive web visualization tool. EMPress addresses the growing need for exploratory analysis tools that can accommodate large, complex multi-omic data sets.

A new phylogenetic protocol: dealing with model misspecification and confirmation bias in molecular phylogenetics

Molecular phylogenetics plays a key role in comparative genomics and has increasingly significant impacts on science, industry, government, public health and society. In this paper, we posit that the current phylogenetic protocol is missing two critical steps, and that their absence allows model misspecification and confirmation bias to unduly influence phylogenetic estimates. Based on the potential offered by well-established but under-used procedures, such as assessment of phylogenetic assumptions and tests of goodness of fit, we introduce a new phylogenetic protocol that will reduce confirmation bias and increase the accuracy of phylogenetic estimates.

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g., CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open-source license.

Advances in monitoring soil microbial community dynamic and function

Microorganisms are vital to the overall ecosystem functioning, stability, and sustainability. Soil fertility and health depend on chemical composition and also on the qualitative and quantitative nature of microorganisms inhabiting it. Historically, denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE), single-strand conformation polymorphism, DNA amplification fingerprinting, amplified ribosomal DNA restriction analysis, terminal restriction fragment length polymorphism, length heterogeneity PCR, and ribosomal intergenic spacer analysis were used to assess soil microbial community structure (SMCS), abundance, and diversity. However, these methods had significant shortcomings and limitations for application in land reclamation monitoring. SMCS has been primarily determined by phospholipid fatty acid (PLFA) analysis. This method provides a direct measure of viable biomass in addition to a biochemical profile of the microbial community. PLFA has limitations such as overlap in the composition of microorganisms and the specificity of PLFAs signature. In recent years, high-throughput next-generation sequencing has dramatically increased the resolution and detectable spectrum of diverse microbial phylotypes from environmental samples and it plays a significant role in microbial ecology studies. Next-generation sequencings using 454, Illumina, SOLiD, and Ion Torrent platforms are rapid and flexible. The two methods, PLFA and next-generation sequencing, are useful in detecting changes in microbial community diversity and structure in different ecosystems. Single-molecule real-time (SMRT) and nanopore sequencing technologies represent third-generation sequencing (TGS) platforms that have been developed to address the shortcomings of second-generation sequencing (SGS). Enzymatic and soil respiration analyses are performed to further determine soil quality and microbial activities. Other valuable methods that are being recently applied to microbial function and structures include NanoSIM, GeoChip, and DNA stable staple isotope probing (DNA-SIP) technologies. They are powerful metagenomics tool for analyzing microbial communities, including their structure, metabolic potential, diversity, and their impact on ecosystem functions. This review is a critical analysis of current methods used in monitoring soil microbial community dynamic and functions.

Poultry processing and the application of microbiome mapping

Chicken is globally one of the most popular food animals. However, it is also one of the major reservoirs for foodborne pathogens, annually resulting in continued morbidity and mortality incidences worldwide. In an effort to reduce the threat of foodborne disease, the poultry industry has implemented a multifaceted antimicrobial program that incorporates not only chemical compounds, but also extensive amounts of water application and pathogen monitoring. Unfortunately, the pathogen detection methods currently used by the poultry industry lack speed, relying on microbiological plate methods and molecular detection systems that take time and lack precision. In many cases, the time to data acquisition can take 12 to 24 h. This is problematic if shorter-term answers are required which is becoming more likely as the public demand for chicken meat is only increasing, leading to new pressures to increase line speed. Therefore, new innovations in detection methods must occur to mitigate the risk of foodborne pathogens that could result from faster slaughter and processing speeds. Future technology will have 2 tracks: rapid methods that are meant to detect pathogens and indicator organisms within a few hours, and long-term methods that use microbiome mapping to evaluate sanitation and antimicrobial efficacy. Together, these methods will provide rapid, comprehensive data capable of being applied in both risk-assessment algorithms and used by management to safeguard the public.

Modulation of methotrexate-induced intestinal mucosal injury by dietary factors

Methotrexate (MTX)-induced intestinal mucosal injury in animals has been studied to understand how MTX can cause gastrointestinal disorders, but the pathogenesis of gastrointestinal disorders is still uncertain. We have attempted to reveal how dietary factors influence intestinal toxicity due to MTX. Mice were fed normal chow (NC) or a high-fat high-sucrose diet (HFHSD) before oral administration of MTX. While MTX significantly decreased the survival rates of mice fed HFHSD, the intestinal epithelial injury was detected. MTX excretion in the feces of mice fed HFHSD was reduced. Change of diets between NC and HFHSD influences the survival. The survival rates of the mice fed a high-sucrose diet or control diet were higher than those fed HFHSD. Higher survival rates were observed in mice fed a high-fat high-sucrose diet modified (HFHSD-M) in which casein was replaced by soybean-derived proteins. The survival rates of mice treated with vancomycin were lower than those administered neomycin. Microbiome and metabolome analyses on feces suggest a similarity of the intestinal environments of mice fed NC and HFHSD-M. HFHSD may modify MTX-induced toxicity in intestinal epithelia on account of an altered MTX distribution as a result of change in the intestinal environment.

Soil characteristics and land-use drive bacterial community assembly patterns

Land-use and soil characteristics drive variation in soil community composition, but the influences of these factors on dispersal and community assembly at regional scale remain poorly characterized. Land-use remains a consistent driver of soil community composition even when exhibiting patchy spatial distribution at regional scale. In addition, disturbed and early successional soils often exhibit stochastic community assembly patterns. These observations suggest local community composition is influenced by dispersal and assembly from regional species pools. We examined bacterial community assembly within agricultural cropland, old-field, and forested sites across 10 landscapes in the region around Ithaca, New York (USA). We found that the Sloan neutral model explained assembly well at regional scale (R2 = 0.763), but that both soil pH and land-use imposed selection that shaped community composition. We show that homogeneous selection was a dominant assembly process with respect to both soil pH and land-use regime, but that these two factors interacted in their effects on bacterial community assembly. We conclude that bacterial community assembly at a regional scale is driven by dispersal from regional species pools and local selection on the basis of soil pH and other soil characteristics that vary with land-use.

The Prevalence and Impact of Model Violations in Phylogenetic Analysis

In phylogenetic inference, we commonly use models of substitution which assume that sequence evolution is stationary, reversible, and homogeneous (SRH). Although the use of such models is often criticized, the extent of SRH violations and their effects on phylogenetic inference of tree topologies and edge lengths are not well understood. Here, we introduce and apply the maximal matched-pairs tests of homogeneity to assess the scale and impact of SRH model violations on 3,572 partitions from 35 published phylogenetic data sets. We show that roughly one-quarter of all the partitions we analyzed (23.5%) reject the SRH assumptions, and that for 25% of data sets, tree topologies inferred from all partitions differ significantly from topologies inferred using the subset of partitions that do not reject the SRH assumptions. This proportion increases when comparing trees inferred using the subset of partitions that rejects the SRH assumptions, to those inferred from partitions that do not reject the SRH assumptions. These results suggest that the extent and effects of model violation in phylogenetics may be substantial. They highlight the importance of testing for model violations and possibly excluding partitions that violate models prior to tree reconstruction. Our results also suggest that further effort in developing models that do not require SRH assumptions could lead to large improvements in the accuracy of phylogenomic inference. The scripts necessary to perform the analysis are available in https://github.com/roblanf/SRHtests, and the new tests we describe are available as a new option in IQ-TREE (http://www.iqtree.org).

Comparative Characterization of Bacterial Communities in Moss-Covered and Unvegetated Volcanic Deposits of Mount Merapi, Indonesia

Microbial colonization, followed by succession, on newly exposed volcanic substrates represents the beginning of the development of an early ecosystem. During early succession, colonization by mosses or plants significantly alters the pioneer microbial community composition through the photosynthetic carbon input. To provide further insights into this process, we investigated the three-year-old volcanic deposits of Mount Merapi, Indonesia. Samples were collected from unvegetated (BRD) and moss-covered (BRUD) sites. Forest site soil (FRS) near the volcanic deposit-covered area was also collected for reference. An analysis of BRD and BRUD revealed high culturable cell densities (1.7-8.5×105 CFU g-1) despite their low total C (<0.01%). FRS possessed high CFU (3×106 g-1); however, its relative value per unit of total C (2.6%) was lower than that of the deposit samples. Based on the tag pyrosequencing of 16S rRNA genes, the BRD bacterial community was characterized by a higher number of betaproteobacterial families (or genus), represented by chemolithotrophic Methylophilaceae, Leptothrix, and Sulfuricellaceae. In contrast, BRUD was predominated by different betaproteobacterial families, such as Oxalobacteraceae, Comamonadaceae, and Rhodocyclaceae. Some bacterial (Oxalobacteraceae) sequences were phylogenetically related to those of known moss-associated bacteria. Within the FRS community, Proteobacteria was the most abundant phylum, followed by Acidobacteria, whereas Burkholderiaceae was the most dominant bacterial family within FRS. These results suggest that an inter-family succession of Betaproteobacteria occurred in response to colonization by mosses, followed by plants.

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

Taxonomic and Functional Annotation of Termite Degraded Butea monosperma (Lam.) Kuntze (Flame of the Forest)

Background:

Butea monosperma is an economically and medicinally important plant that grows all over India, however, the plant is highly susceptible to termite attack. The present study unravelled the bacterial community composition and their functional attributions from the termite degraded Butea.

Methods:

Total genomic DNA from termite degraded Butea monosperma samples was extracted and subjected to sequencing on Illumina's Miseq. The raw and unassembled reads obtained from high-throughput sequencing were used for taxonomic and functional profiling using different online and stand-alone softwares. Moreover, to ascertain the effect of different geographical locations and environmental factors, comparative analysis was performed using four other publically available metagenomes.

Results:

The higher abundance of Actinobacteria (21.27%), Proteobacteria (14.18%), Firmicutes (10.46%), and Bacteroidetes (4.11%) was found at the phylum level. The genus level was dominated by Bacillus (4.33%), Gemmatimonas (3.13%), Mycobacterium (1.82%), Acidimicrobium (1.69%), Thermoleophilum (1.23%), Nocardioides (1.44%), Terrimonas and Acidithermus (1.09%) and Clostridium (1.05%). Functional annotation of the termite degraded B. monosperma metagenome revealed a high abundance of ammonia oxidizers, sulfate reducers, dehalogenators, nitrate reducers, sulfide oxidizers, xylan degraders, nitrogen fixers and chitin degraders.

Conclusion:

The present study highlights the significance of the inherent microbiome of the degraded Butea shaping the microbial communities for effective degradation of biomass and different environmental toxicants. The unknown bacterial communities present in the sample can serve as enzyme sources for lignocelluloses degradation for biofuel production.

Network Analysis of Protein Adaptation: Modeling the Functional Impact of Multiple Mutations

The evolution of new biochemical activities frequently involves complex dependencies between mutations and rapid evolutionary radiation. Mutation co-occurrence and covariation have previously been used to identify compensating mutations that are the result of physical contacts and preserve protein function and fold. Here, we model pairwise functional dependencies and higher order interactions that enable evolution of new protein functions. We use a network model to find complex dependencies between mutations resulting from evolutionary trade-offs and pleiotropic effects. We present a method to construct these networks and to identify functionally interacting mutations in both extant and reconstructed ancestral sequences (Network Analysis of Protein Adaptation). The time ordering of mutations can be incorporated into the networks through phylogenetic reconstruction. We apply NAPA to three distantly homologous β-lactamase protein clusters (TEM, CTX-M-3, and OXA-51), each of which has experienced recent evolutionary radiation under substantially different selective pressures. By analyzing the network properties of each protein cluster, we identify key adaptive mutations, positive pairwise interactions, different adaptive solutions to the same selective pressure, and complex evolutionary trajectories likely to increase protein fitness. We also present evidence that incorporating information from phylogenetic reconstruction and ancestral sequence inference can reduce the number of spurious links in the network, whereas preserving overall network community structure. The analysis does not require structural or biochemical data. In contrast to function-preserving mutation dependencies, which are frequently from structural contacts, gain-of-function mutation dependencies are most commonly between residues distal in protein structure.

3D based on 2D: Calculating helix angles and stacking patterns using forgi 2.0, an RNA Python library centered on secondary structure elements

We present forgi, a Python library to analyze the tertiary structure of RNA secondary structure elements. Our representation of an RNA molecule is centered on secondary structure elements (stems, bulges and loops). By fitting a cylinder to the helix axis, these elements are carried over into a coarse-grained 3D structure representation. Integration with Biopython allows for handling of all-atom 3D information. forgi can deal with a variety of file formats including dotbracket strings, PDB and MMCIF files. We can handle modified residues, missing residues, cofold and multifold structures as well as nucleotide numbers starting at arbitrary positions. We apply this library to the study of stacking helices in junctions and pseudoknots and investigate how far stacking helices in solved experimental structures can divert from coaxial geometries.

3D based on 2D: Calculating helix angles and stacking patterns using forgi 2.0, an RNA Python library centered on secondary structure elements

We present forgi, a Python library to analyze the tertiary structure of RNA secondary structure elements. Our representation of an RNA molecule is centered on secondary structure elements (stems, bulges and loops). By fitting a cylinder to the helix axis, these elements are carried over into a coarse-grained 3D structure representation. Integration with Biopython allows for handling of all-atom 3D information. forgi can deal with a variety of file formats including dotbracket strings, PDB and MMCIF files. We can handle modified residues, missing residues, cofold and multifold structures as well as nucleotide numbers starting at arbitrary positions. We apply this library to the study of stacking helices in junctions and pseudo knots and investigate how far stacking helices in solved experimental structures can divert from coaxial geometries.

Emergence of trait variability through the lens of nitrogen assimilation in Prochlorococcus

Intraspecific trait variability has important consequences for the function and stability of marine ecosystems. Here we examine variation in the ability to use nitrate across hundreds of Prochlorococcus genomes to better understand the modes of evolution influencing intraspecific allocation of ecologically important functions. Nitrate assimilation genes are absent in basal lineages but occur at an intermediate frequency that is randomly distributed within recently emerged clades. The distribution of nitrate assimilation genes within clades appears largely governed by vertical inheritance, gene loss, and homologous recombination. By mapping this process onto a model of Prochlorococcus’ macroevolution, we propose that niche-constructing adaptive radiations and subsequent niche partitioning set the stage for loss of nitrate assimilation genes from basal lineages as they specialized to lower light levels. Retention of these genes in recently emerged lineages has likely been facilitated by selection as they sequentially partitioned into niches where nitrate assimilation conferred a fitness benefit.

Establishment of a Standardized 16S rDNA Library Preparation to Enable Analysis of Microbiome in Poultry Processing Using Illumina MiSeq Platform

The standardization of the microbiome sequencing of poultry rinsates is essential for generating comparable microbial composition data among poultry processing facilities if this technology is to be adopted by the industry. Samples must first be acquired, DNA must be extracted, and libraries must be constructed. In order to proceed to library sequencing, the samples should meet quality control standards. Finally, data must be analyzed using computer bioinformatics pipelines. This data can subsequently be incorporated into more advanced computer algorithms for risk assessment. Ultimately, a uniform sequencing pipeline will enable both the government regulatory agencies and the poultry industry to identify potential weaknesses in food safety. This chapter presents the different steps for monitoring the population dynamics of the microbiome in poultry processing using 16S rDNA sequencing.

A comprehensive fungi-specific 18S rRNA gene sequence primer toolkit suited for diverse research issues and sequencing platforms

BACKGROUND

Several fungi-specific primers target the 18S rRNA gene sequence, one of the prominent markers for fungal classification. The design of most primers goes back to the last decades. Since then, the number of sequences in public databases increased leading to the discovery of new fungal groups and changes in fungal taxonomy. However, no reevaluation of primers was carried out and relevant information on most primers is missing. With this study, we aimed to develop an 18S rRNA gene sequence primer toolkit allowing an easy selection of the best primer pair appropriate for different sequencing platforms, research aims (biodiversity assessment versus isolate classification) and target groups.

RESULTS

We performed an intensive literature research, reshuffled existing primers into new pairs, designed new Illumina-primers, and annealing blocking oligonucleotides. A final number of 439 primer pairs were subjected to in silico PCRs. Best primer pairs were selected and experimentally tested. The most promising primer pair with a small amplicon size, nu-SSU-1333-5'/nu-SSU-1647-3' (FF390/FR-1), was successful in describing fungal communities by Illumina sequencing. Results were confirmed by a simultaneous metagenomics and eukaryote-specific primer approach. Co-amplification occurred in all sample types but was effectively reduced by blocking oligonucleotides.

CONCLUSIONS

The compiled data revealed the presence of an enormous diversity of fungal 18S rRNA gene primer pairs in terms of fungal coverage, phylum spectrum and co-amplification. Therefore, the primer pair has to be carefully selected to fulfill the requirements of the individual research projects. The presented primer toolkit offers comprehensive lists of 164 primers, 439 primer combinations, 4 blocking oligonucleotides, and top primer pairs holding all relevant information including primer's characteristics and performance to facilitate primer pair selection.

Comparative genomics reveals the distinct evolutionary trajectories of the robust and complex coral lineages

BACKGROUND

Despite the biological and economic significance of scleractinian reef-building corals, the lack of large molecular datasets for a representative range of species limits understanding of many aspects of their biology. Within the Scleractinia, based on molecular evidence, it is generally recognised that there are two major clades, Complexa and Robusta, but the genomic bases of significant differences between them remain unclear.

RESULTS

Draft genome assemblies and annotations were generated for three coral species: Galaxea fascicularis (Complexa), Fungia sp., and Goniastrea aspera (Robusta). Whilst phylogenetic analyses strongly support a deep split between Complexa and Robusta, synteny analyses reveal a high level of gene order conservation between all corals, but not between corals and sea anemones or between sea anemones. HOX-related gene clusters are, however, well preserved across all of these combinations. Differences between species are apparent in the distribution and numbers of protein domains and an apparent correlation between number of HSP20 proteins and stress tolerance. Uniquely amongst animals, a complete histidine biosynthesis pathway is present in robust corals but not in complex corals or sea anemones. This pathway appears to be ancestral, and its retention in the robust coral lineage has important implications for coral nutrition and symbiosis.

CONCLUSIONS

The availability of three new coral genomes enabled recognition of a de novo histidine biosynthesis pathway in robust corals which is only the second identified biosynthetic difference between corals. These datasets provide a platform for understanding many aspects of coral biology, particularly the interactions of corals with their endosymbionts.

A k-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

We have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) identifies phenotype-specific k-mers, (b) generates a k-mer-based statistical model for predicting a given phenotype and (c) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167 Klebsiella pneumoniae isolates (virulence), 200 Pseudomonas aeruginosa isolates (ciprofloxacin resistance) and 459 Clostridium difficile isolates (azithromycin resistance). The phenotype prediction models trained from these datasets obtained the F1-measure of 0.88 on the K. pneumoniae test set, 0.88 on the P. aeruginosa test set and 0.97 on the C. difficile test set. The F1-measures were the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets. PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).

Biotite: a unifying open source computational biology framework in Python

BACKGROUND

As molecular biology is creating an increasing amount of sequence and structure data, the multitude of software to analyze this data is also rising. Most of the programs are made for a specific task, hence the user often needs to combine multiple programs in order to reach a goal. This can make the data processing unhandy, inflexible and even inefficient due to an overhead of read/write operations. Therefore, it is crucial to have a comprehensive, accessible and efficient computational biology framework in a scripting language to overcome these limitations.

RESULTS

We have developed the Python package Biotite: a general computational biology framework, that represents sequence and structure data based on NumPyndarrays. Furthermore the package contains seamless interfaces to biological databases and external software. The source code is freely accessible at https://github.com/biotite-dev/biotite .

CONCLUSIONS

Biotite is unifying in two ways: At first it bundles popular tasks in sequence analysis and structural bioinformatics in a consistently structured package. Secondly it adresses two groups of users: novice programmers get an easy access to Biotite due to its simplicity and the comprehensive documentation. On the other hand, advanced users can profit from its high performance and extensibility. They can implement their algorithms upon Biotite, so they can skip writing code for general functionality (like file parsers) and can focus on what their software makes unique.

Did aculeate silk evolve as an antifouling material?

Many of the challenges we currently face as an advanced society have been solved in unique ways by biological systems. One such challenge is developing strategies to avoid microbial infection. Social aculeates (wasps, bees and ants) mitigate the risk of infection to their colonies using a wide range of adaptations and mechanisms. These adaptations and mechanisms are reliant on intricate social structures and are energetically costly for the colony. It seems likely that these species must have had alternative and simpler mechanisms in place to ensure the maintenance of hygienic domicile conditions prior to the evolution of these complex behaviours. Features of the aculeate coiled-coil silk proteins are reminiscent of those of naturally occurring α-helical antimicrobial peptides (AMPs). In this study, we demonstrate that peptides derived from the aculeate silk proteins have antimicrobial activity. We reconstruct the predicted ancestral silk sequences of an aculeate ancestor that pre-dates the evolution of sociality and demonstrate that these ancestral sequences also contained peptides with antimicrobial properties. It is possible that the silks evolved as an antifouling material and facilitated the evolution of sociality. These materials serve as model materials for consideration in future biomaterial development.

Standard Codon Substitution Models Overestimate Purifying Selection for Nonstationary Data

Estimation of natural selection on protein-coding sequences is a key comparative genomics approach for de novo prediction of lineage-specific adaptations. Selective pressure is measured on a per-gene basis by comparing the rate of nonsynonymous substitutions to the rate of synonymous substitutions. All published codon substitution models have been time-reversible and thus assume that sequence composition does not change over time. We previously demonstrated that if time-reversible DNA substitution models are applied in the presence of changing sequence composition, the number of substitutions is systematically biased towards overestimation. We extend these findings to the case of codon substitution models and further demonstrate that the ratio of nonsynonymous to synonymous rates of substitution tends to be underestimated over three data sets of mammals, vertebrates, and insects. Our basis for comparison is a nonstationary codon substitution model that allows sequence composition to change. Goodness-of-fit results demonstrate that our new model tends to fit the data better. Direct measurement of nonstationarity shows that bias in estimates of natural selection and genetic distance increases with the degree of violation of the stationarity assumption. Additionally, inferences drawn under time-reversible models are systematically affected by compositional divergence. As genomic sequences accumulate at an accelerating rate, the importance of accurate de novo estimation of natural selection increases. Our results establish that our new model provides a more robust perspective on this fundamental quantity.

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model

In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clusters is not required here. For the sake of illustration, this method is applied on a set of 100 DNA sequences taken from the mitochondrially encoded NADH dehydrogenase 3 (ND3) gene, extracted from a collection of Platyhelminthes and Nematoda species. The resulting clusters are tightly consistent with the phylogenetic tree computed using a maximum likelihood approach on gene alignment. They are coherent too with the NCBI taxonomy. Further test results based on synthesized data are then provided, showing that the proposed approach is better able to recover the clusters than the most widely used software, namely Cd-hit-est and BLASTClust.

Assessing the utility of metabarcoding for diet analyses of the omnivorous wild pig (Sus scrofa)

Wild pigs (Sus scrofa) are an invasive species descended from both domestic swine and Eurasian wild boar that was introduced to North America during the early 1500s. Wild pigs have since become the most abundant free‐ranging exotic ungulate in the United States. Large and ever‐increasing populations of wild pigs negatively impact agriculture, sport hunting, and native ecosystems with costs estimated to exceed $1.5 billion/year within the United States. Wild pigs are recognized as generalist feeders, able to exploit a broad array of locally available food resources, yet their feeding behaviors remain poorly understood as partially digested material is often unidentifiable through traditional stomach content analyses. To overcome the limitation of stomach content analyses, we developed a DNA sequencing‐based protocol to describe the plant and animal diet composition of wild pigs. Additionally, we developed and evaluated blocking primers to reduce the amplification and sequencing of host DNA, thus providing greater returns of sequences from diet items. We demonstrate that the use of blocking primers produces significantly more sequencing reads per sample from diet items, which increases the robustness of ascertaining animal diet composition with molecular tools. Further, we show that the overall plant and animal diet composition is significantly different between the three areas sampled, demonstrating this approach is suitable for describing differences in diet composition among the locations.

A Critical Review on the Use of Support Values in Tree Viewers and Bioinformatics Toolkits

Phylogenetic trees are routinely visualized to present and interpret the evolutionary relationships of species. Most empirical evolutionary data studies contain a visualization of the inferred tree with branch support values. Ambiguous semantics in tree file formats can lead to erroneous tree visualizations and therefore to incorrect interpretations of phylogenetic analyses. Here, we discuss problems that arise when displaying branch values on trees after rerooting. Branch values are typically stored as node labels in the widely-used Newick tree format. However, such values are attributes of branches. Storing them as node labels can therefore yield errors when rerooting trees. This depends on the mostly implicit semantics that tools deploy to interpret node labels. We reviewed ten tree viewers and ten bioinformatics toolkits that can display and reroot trees. We found that 14 out of 20 of these tools do not permit users to select the semantics of node labels. Thus, unaware users might obtain incorrect results when rooting trees. We illustrate such incorrect mappings for several test cases and real examples taken from the literature. This review has already led to improvements in eight tools. We suggest tools should provide options that explicitly force users to define the semantics of node labels.

Full reconstruction of non-stationary strand-symmetric models on rooted phylogenies

Understanding the evolutionary relationship among species is of fundamental importance to the biological sciences. The location of the root in any phylogenetic tree is critical as it gives an order to evolutionary events. None of the popular models of nucleotide evolution currently used in likelihood or Bayesian methods are able to infer the location of the root without exogenous information. It is known that the most general Markov models of nucleotide substitution also cannot identify the location of the root or be fitted to multiple sequence alignments with fewer than three sequences. We prove that the location of the root and the full model can be identified and statistically consistently estimated for a non-stationary, strand-symmetric substitution model given a multiple sequence alignment with two or more sequences. We also generalise earlier work to provide a practical means of overcoming the computationally intractable problem of labelling hidden states in a phylogenetic model.

Diversity and enterotype in gut bacterial community of adults in Taiwan

Background

Gastrointestinal microbiota, particularly gut microbiota, is associated with human health. The biodiversity of gut microbiota is affected by ethnicities and environmental factors such as dietary habits or medicine intake, and three enterotypes of the human gut microbiome were announced in 2011. These enterotypes are not significantly correlated with gender, age, or body weight but are influenced by long-term dietary habits. However, to date, only two enterotypes (predominantly consisting of Bacteroides and Prevotella) have shown these characteristics in previous research; the third enterotype remains ambiguous. Understanding the enterotypes can improve the knowledge of the relationship between microbiota and human health.

Results

We obtained 181 human fecal samples from adults in Taiwan. Microbiota compositions were analyzed using next-generation sequencing (NGS) technology, which is a culture-independent method of constructing microbial community profiles by sequencing 16S ribosomal DNA (rDNA). In these samples, 17,675,898 sequencing reads were sequenced, and on average, 215 operational taxonomic units (OTUs) were identified for each sample. In this study, the major bacteria in the enterotypes identified from the fecal samples were Bacteroides, Prevotella, and Enterobacteriaceae, and their correlation with dietary habits was confirmed. A microbial interaction network in the gut was observed on the basis of the amount of short-chain fatty acids, pH value of the intestine, and composition of the bacterial community (enterotypes). Finally, a decision tree was derived to provide a predictive model for the three enterotypes. The accuracies of this model in training and independent testing sets were 97.2 and 84.0%, respectively.

Conclusions

We used NGS technology to characterize the microbiota and constructed a predictive model. The most significant finding was that Enterobacteriaceae, the predominant subtype, could be a new subtype of enterotypes in the Asian population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3261-6) contains supplementary material, which is available to authorized users.

Statistical Methods for Identifying Sequence Motifs Affecting Point Mutations

Mutation processes differ between types of point mutation, genomic locations, cells, and biological species. For some point mutations, specific neighboring bases are known to be mechanistically influential. Beyond these cases, numerous questions remain unresolved, including: what are the sequence motifs that affect point mutations? How large are the motifs? Are they strand symmetric? And, do they vary between samples? We present new log-linear models that allow explicit examination of these questions, along with sequence logo style visualization to enable identifying specific motifs. We demonstrate the performance of these methods by analyzing mutation processes in human germline and malignant melanoma. We recapitulate the known CpG effect, and identify novel motifs, including a highly significant motif associated with A[Formula: see text]G mutations. We show that major effects of neighbors on germline mutation lie within [Formula: see text] of the mutating base. Models are also presented for contrasting the entire mutation spectra (the distribution of the different point mutations). We show the spectra vary significantly between autosomes and X-chromosome, with a difference in T[Formula: see text]C transition dominating. Analyses of malignant melanoma confirmed reported characteristic features of this cancer, including statistically significant strand asymmetry, and markedly different neighboring influences. The methods we present are made freely available as a Python library https://bitbucket.org/pycogent3/mutationmotif.

Desert Perennial Shrubs Shape the Microbial-Community Miscellany in Laimosphere and Phyllosphere Space

Microbial function, composition, and distribution play a fundamental role in ecosystem ecology. The interaction between desert plants and their associated microbes is expected to greatly affect their response to changes in this harsh environment. Using comparative analyses, we studied the impact of three desert shrubs, Atriplex halimus (A), Artemisia herba-alba (AHA), and Hammada scoparia (HS), on soil- and leaf-associated microbial communities. DNA extracted from the leaf surface and soil samples collected beneath the shrubs were used to study associated microbial diversity using a sequencing survey of variable regions of bacterial 16S rRNA and fungal ribosomal internal transcribed spacer (ITS1). We found that the composition of bacterial and fungal orders is plant-type-specific, indicating that each plant type provides a suitable and unique microenvironment. The different adaptive ecophysiological properties of the three plant species and the differential effect on their associated microbial composition point to the role of adaptation in the shaping of microbial diversity. Overall, our findings suggest a link between plant ecophysiological adaptation as a "temporary host" and the biotic-community parameters in extreme xeric environments.

Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes

Adaptive radiations are important drivers of niche filling, since they rapidly adapt a single clade of organisms to ecological opportunities. Although thought to be common for animals and plants, adaptive radiations have remained difficult to document for microbes in the wild. Here we describe a recent adaptive radiation leading to fine-scale ecophysiological differentiation in the degradation of an algal glycan in a clade of closely related marine bacteria. Horizontal gene transfer is the primary driver in the diversification of the pathway leading to several ecophysiologically differentiated Vibrionaceae populations adapted to different physical forms of alginate. Pathway architecture is predictive of function and ecology, underscoring that horizontal gene transfer without extensive regulatory changes can rapidly assemble fully functional pathways in microbes.

Adaptive radiations are well-known for animals and plants, but not for microbes. Here, Hehemann et al. show that there has been a recent adaptive radiation of bacteria in the Vibrionaceae to use different forms of alginate and that this radiation has been mediated by horizontal gene transfer.

Positive Selection Linked with Generation of Novel Mammalian Dentition Patterns

A diverse group of genes are involved in the tooth development of mammals. Several studies, focused mainly on mice and rats, have provided a detailed depiction of the processes coordinating tooth formation and shape. Here we surveyed 236 tooth-associated genes in 39 mammalian genomes and tested for signatures of selection to assess patterns of molecular adaptation in genes regulating mammalian dentition. Of the 236 genes, 31 (∼13.1%) showed strong signatures of positive selection that may be responsible for the phenotypic diversity observed in mammalian dentition. Mammalian-specific tooth-associated genes had accelerated mutation rates compared with older genes found across all vertebrates. More recently evolved genes had fewer interactions (either genetic or physical), were associated with fewer Gene Ontology terms and had faster evolutionary rates compared with older genes. The introns of these positively selected genes also exhibited accelerated evolutionary rates, which may reflect additional adaptive pressure in the intronic regions that are associated with regulatory processes that influence tooth-gene networks. The positively selected genes were mainly involved in processes like mineralization and structural organization of tooth specific tissues such as enamel and dentin. Of the 236 analyzed genes, 12 mammalian-specific genes (younger genes) provided insights on diversification of mammalian teeth as they have higher evolutionary rates and exhibit different expression profiles compared with older genes. Our results suggest that the evolution and development of mammalian dentition occurred in part through positive selection acting on genes that previously had other functions.

ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data

The Environment for Tree Exploration (ETE) is a computational framework that simplifies the reconstruction, analysis, and visualization of phylogenetic trees and multiple sequence alignments. Here, we present ETE v3, featuring numerous improvements in the underlying library of methods, and providing a novel set of standalone tools to perform common tasks in comparative genomics and phylogenetics. The new features include (i) building gene-based and supermatrix-based phylogenies using a single command, (ii) testing and visualizing evolutionary models, (iii) calculating distances between trees of different size or including duplications, and (iv) providing seamless integration with the NCBI taxonomy database. ETE is freely available at http://etetoolkit.org.

pyGeno: A Python package for precision medicine and proteogenomics

pyGeno is a Python package mainly intended for precision medicine applications that revolve around genomics and proteomics. It integrates reference sequences and annotations from Ensembl, genomic polymorphisms from the dbSNP database and data from next-gen sequencing into an easy to use, memory-efficient and fast framework, therefore allowing the user to easily explore subject-specific genomes and proteomes. Compared to a standalone

program, pyGeno gives the user access to the complete expressivity of Python, a general programming language. Its range of application therefore encompasses both short scripts and large scale genome-wide studies.

pyGeno: A Python package for precision medicine and proteogenomics

pyGeno is a Python package mainly intended for precision medicine applications that revolve around genomics and proteomics. It integrates reference sequences and annotations from Ensembl, genomic polymorphisms from the dbSNP database and data from next-gen sequencing into an easy to use, memory-efficient and fast framework, therefore allowing the user to easily explore subject-specific genomes and proteomes. Compared to a standalone program, pyGeno gives the user access to the complete expressivity of Python, a general programming language. Its range of application therefore encompasses both short scripts and large scale genome-wide studies.

A Fungal Effector With Host Nuclear Localization and DNA-Binding Properties Is Required for Maize Anthracnose Development

Plant pathogens have the capacity to manipulate the host immune system through the secretion of effectors. We identified 27 putative effector proteins encoded in the genome of the maize anthracnose pathogen Colletotrichum graminicola that are likely to target the host's nucleus, as they simultaneously contain sequence signatures for secretion and nuclear localization. We functionally characterized one protein, identified as CgEP1. This protein is synthesized during the early stages of disease development and is necessary for anthracnose development in maize leaves, stems, and roots. Genetic, molecular, and biochemical studies confirmed that this effector targets the host's nucleus and defines a novel class of double-stranded DNA-binding protein. We show that CgEP1 arose from a gene duplication in an ancestor of a lineage of monocot-infecting Colletotrichum spp. and has undergone an intense evolution process, with evidence for episodes of positive selection. We detected CgEP1 homologs in several species of a grass-infecting lineage of Colletotrichum spp., suggesting that its function may be conserved across a large number of anthracnose pathogens. Our results demonstrate that effectors targeted to the host nucleus may be key elements for disease development and aid in the understanding of the genetic basis of anthracnose development in maize plants.

Non-cyanobacterial diazotrophs mediate dinitrogen fixation in biological soil crusts during early crust formation

Biological soil crusts (BSCs) are key components of ecosystem productivity in arid lands and they cover a substantial fraction of the terrestrial surface. In particular, BSC N2-fixation contributes significantly to the nitrogen (N) budget of arid land ecosystems. In mature crusts, N2-fixation is largely attributed to heterocystous cyanobacteria; however, early successional crusts possess few N2-fixing cyanobacteria and this suggests that microorganisms other than cyanobacteria mediate N2-fixation during the critical early stages of BSC development. DNA stable isotope probing with (15)N2 revealed that Clostridiaceae and Proteobacteria are the most common microorganisms that assimilate (15)N2 in early successional crusts. The Clostridiaceae identified are divergent from previously characterized isolates, though N2-fixation has previously been observed in this family. The Proteobacteria identified share >98.5% small subunit rRNA gene sequence identity with isolates from genera known to possess diazotrophs (for example, Pseudomonas, Klebsiella, Shigella and Ideonella). The low abundance of these heterotrophic diazotrophs in BSCs may explain why they have not been characterized previously. Diazotrophs have a critical role in BSC formation and characterization of these organisms represents a crucial step towards understanding how anthropogenic change will affect the formation and ecological function of BSCs in arid ecosystems.

Application of computational methods in genetic study of inflammatory bowel disease

Genetic factors play an important role in the etiology of inflammatory bowel disease (IBD). The launch of genome-wide association study (GWAS) represents a landmark in the genetic study of human complex disease. Concurrently, computational methods have undergone rapid development during the past a few years, which led to the identification of numerous disease susceptibility loci. IBD is one of the successful examples of GWAS and related analyses. A total of 163 genetic loci and multiple signaling pathways have been identified to be associated with IBD. Pleiotropic effects were found for many of these loci; and risk prediction models were built based on a broad spectrum of genetic variants. Important gene-gene, gene-environment interactions and key contributions of gut microbiome are being discovered. Here we will review the different types of analyses that have been applied to IBD genetic study, discuss the computational methods for each type of analysis, and summarize the discoveries made in IBD research with the application of these methods.

Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli

Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.

PyPedia: using the wiki paradigm as crowd sourcing environment for bioinformatics protocols

Background

Today researchers can choose from many bioinformatics protocols for all types of life sciences research, computational environments and coding languages. Although the majority of these are open source, few of them possess all virtues to maximize reuse and promote reproducible science. Wikipedia has proven a great tool to disseminate information and enhance collaboration between users with varying expertise and background to author qualitative content via crowdsourcing. However, it remains an open question whether the wiki paradigm can be applied to bioinformatics protocols.

Results

We piloted PyPedia, a wiki where each article is both implementation and documentation of a bioinformatics computational protocol in the python language. Hyperlinks within the wiki can be used to compose complex workflows and induce reuse. A RESTful API enables code execution outside the wiki. Initial content of PyPedia contains articles for population statistics, bioinformatics format conversions and genotype imputation. Use of the easy to learn wiki syntax effectively lowers the barriers to bring expert programmers and less computer savvy researchers on the same page.

Conclusions

PyPedia demonstrates how wiki can provide a collaborative development, sharing and even execution environment for biologists and bioinformaticians that complement existing resources, useful for local and multi-center research teams.

Availability

PyPedia is available online at: http://www.pypedia.com. The source code and installation instructions are available at: https://github.com/kantale/PyPedia_server. The PyPedia python library is available at: https://github.com/kantale/pypedia. PyPedia is open-source, available under the BSD 2-Clause License.

Electronic supplementary material

The online version of this article (doi:10.1186/s13029-015-0042-6) contains supplementary material, which is available to authorized users.

Cnidaria: fast, reference-free clustering of raw and assembled genome and transcriptome NGS data

Background

Identification of biological specimens is a requirement for a range of applications. Reference-free methods analyse unprocessed sequencing data without relying on prior knowledge, but generally do not scale to arbitrarily large genomes and arbitrarily large phylogenetic distances.

Results

We present Cnidaria, a practical tool for clustering genomic and transcriptomic data with no limitation on genome size or phylogenetic distances. We successfully simultaneously clustered 169 genomic and transcriptomic datasets from 4 kingdoms, achieving 100 % identification accuracy at supra-species level and 78 % accuracy at the species level.

Conclusion

CNIDARIA allows for fast, resource-efficient comparison and identification of both raw and assembled genome and transcriptome data. This can help answer both fundamental (e.g. in phylogeny, ecological diversity analysis) and practical questions (e.g. sequencing quality control, primer design).

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0806-7) contains supplementary material, which is available to authorized users.

The Identification of Circulating MiRNA in Bovine Serum and Their Potential as Novel Biomarkers of Early Mycobacterium avium subsp paratuberculosis Infection

Mycobacterium avium subspecies paratuberculosis (MAP) is the aetiological agent of Johne's disease (JD), a chronic enteritis in ruminants that causes substantial economic loses to agriculture worldwide. Current diagnostic assays are hampered by low sensitivity and specificity that seriously complicate disease control; a new generation of diagnostic and prognostic assays are therefore urgently needed. Circulating microRNAs (miRNAs) have been shown to have significant potential as novel biomarkers for a range of human diseases, but their potential application in the veterinary sphere has been less well characterised. The aim of this study was therefore to apply RNA-sequencing approaches to serum from an experimental JD infection model as a route to identify novel diagnostic and prognostic miRNA biomarkers. Sera from experimental MAP-challenged calves (n = 6) and age-matched controls (n = 6) were used. We identified a subset of known miRNAs from bovine serum across all samples, with approximately 90 being at potentially functional abundance levels. The majority of known bovine miRNAs displayed multiple isomiRs that differed from the canonical sequences. Thirty novel miRNAs were identified after filtering and were found within sera from all animals tested. No significant differential miRNA expression was detected when comparing sera from MAP-challenged animals to their age-matched controls at six-month's post-infection. However, comparing sera from pre-infection bleeds to six-month's post-infection across all 12 animals did identify increased miR-205 (2-fold) and decreased miR-432 (2-fold) within both challenged and control groups, which suggests changes in circulating miRNA profiles due to ageing or development (P<0.00001). In conclusion our study has identified a range of novel miRNA in bovine serum, and shown the utility of small RNA sequencing approaches to explore the potential of miRNA as novel biomarkers for infectious disease in cattle.