Pipeliner: software to evaluate the performance of bioinformatics pipelines for next‐generation resequencing

The choice of technology and bioinformatics approach is critical in obtaining accurate and reliable information from next‐generation sequencing (NGS) experiments. An increasing number of software and methodological guidelines are being published, but deciding upon which approach and experimental design to use can depend on the particularities of the species and on the aims of the study. This leaves researchers unable to produce informed decisions on these central questions. To address these issues, we developed pipeliner – a tool to evaluate, by simulation, the performance of NGS pipelines in resequencing studies. Pipeliner provides a graphical interface allowing the users to write and test their own bioinformatics pipelines with publicly available or custom software. It computes a number of statistics summarizing the performance in SNP calling, including the recovery, sensitivity and false discovery rate for heterozygous and homozygous SNP genotypes. Pipeliner can be used to answer many practical questions, for example, for a limited amount of NGS effort, how many more reliable SNPs can be detected by doubling coverage and halving sample size or what is the false discovery rate provided by different SNP calling algorithms and options. Pipeliner thus allows researchers to carefully plan their study's sampling design and compare the suitability of alternative bioinformatics approaches for their specific study systems. Pipeliner is written in C++ and is freely available from http://github.com/brunonevado/Pipeliner .     

Influence of different data cleaning solutions of point‐occurrence records on downstream macroecological diversity models

Digital point‐occurrence records from the Global Biodiversity Information Facility (GBIF) and other data providers enable a wide range of research in macroecology and biogeography. However, data errors may hamper immediate use. Manual data cleaning is time‐consuming and often unfeasible, given that the databases may contain thousands or millions of records. Automated data cleaning pipelines are therefore of high importance. Taking North American Ephedra as a model, we examined how different data cleaning pipelines (using, e.g., the GBIF web application, and four different R packages) affect downstream species distribution models (SDMs). We also assessed how data differed from expert data. From 13,889 North American Ephedra observations in GBIF, the pipelines removed 31.7% to 62.7% false positives, invalid coordinates, and duplicates, leading to datasets between 9484 (GBIF application) and 5196 records (manual‐guided filtering). The expert data consisted of 704 records, comparable to data from field studies. Although differences in the absolute numbers of records were relatively large, species richness models based on stacked SDMs (S‐SDM) from pipeline and expert data were strongly correlated (mean Pearson''s r across the pipelines: .9986, vs. the expert data: .9173). Our results suggest that all R package‐based pipelines reliably identified invalid coordinates. In contrast, the GBIF‐filtered data still contained both spatial and taxonomic errors. Major drawbacks emerge from the fact that no pipeline fully discovered misidentified specimens without the assistance of taxonomic expert knowledge. We conclude that application‐filtered GBIF data will still need additional review to achieve higher spatial data quality. Achieving high‐quality taxonomic data will require extra effort, probably by thoroughly analyzing the data for misidentified taxa, supported by experts.     

Bringing Web 2.0 to bioinformatics

Enabling deft data integration from numerous, voluminous andheterogeneous data sources is a major bioinformatic challenge.Several approaches have been proposed to address this challenge,including data warehousing and federated databasing. Yet despitethe rise of these approaches, integration of data from multiplesources remains problematic and toilsome. These two approachesfollow a user-to-computer communication model for data exchange,and do not facilitate a broader concept of data sharing or collaborationamong users. In this report, we discuss the potential of Web2.0 technologies to transcend this model and enhance bioinformaticsresearch. We propose a Web 2.0-based Scientific Social Community(SSC) model for the implementation of these technologies. Byestablishing a social, collective and collaborative platformfor data creation, sharing and integration, we promote a webservices-based pipeline featuring web services for computer-to-computerdata exchange as users add value. This pipeline aims to simplifydata integration and creation, to realize automatic analysis,and to facilitate reuse and sharing of data. SSC can fostercollaboration and harness collective intelligence to createand discover new knowledge. In addition to its research potential,we also describe its potential role as an e-learning platformin education. We discuss lessons from information technology,predict the next generation of Web (Web 3.0), and describe itspotential impact on the future of bioinformatics studies.      

Phylogenetic analysis of a biofilm bacterial population in a water pipeline in the Gulf of Mexico

The aim of this study was to assess the bacterial diversity associated with a corrosive biofilm in a steel pipeline from the Gulf of Mexico used to inject marine water into the oil reservoir. Several aerobic and heterotrophic bacteria were isolated and identified by 16S rRNA gene sequence analysis. Metagenomic DNA was also extracted to perform a denaturing gradient gel electrophoresis analysis of ribosomal genes and to construct a 16S rRNA gene metagenomic library. Denaturing gradient gel electrophoresis profiles and ribosomal libraries exhibited a limited bacterial diversity. Most of the species detected in the ribosomal library or isolated from the pipeline were assigned to Proteobacteria (Halomonas spp., Idiomarina spp., Marinobacter aquaeolei, Thalassospira sp., Silicibacter sp. and Chromohalobacter sp.) and Bacilli (Bacillus spp. and Exiguobacterium spp.). This is the first report that associates some of these bacteria with a corrosive biofilm. It is relevant that no sulfate-reducing bacteria were isolated or detected by a PCR-based method. The diversity and relative abundance of bacteria from water pipeline biofilms may contribute to an understanding of the complexity and mechanisms of metal corrosion during marine water injection in oil secondary recovery.     

Candidate gene identification of existing or induced mutations with pipelines applicable to large genomes

Bulked segregant analysis (BSA) is used to identify existing or induced variants that are linked to phenotypes. Although it is widely used in Arabidopsis and rice, it remains challenging for crops with large genomes, such as maize. Moreover, analysis of huge data sets can present a bottleneck linking phenotypes to their molecular basis, especially for geneticists without programming experience. Here, we identified two genes of maize defective kernel mutants with newly developed analysis pipelines that require no programing skills and should be applicable to any large genome. In the 1970s, Neuffer and Sheridan generated a chemically induced defective kernel (dek) mutant collection with the potential to uncover critical genes for seed development. To locate such mutations, the dek phenotypes were introgressed into two inbred lines to take advantage of maize haplotype variations and their sequenced genomes. We generated two pipelines that take fastq files derived from next‐generation (nextGen) paired‐end DNA and cDNA sequencing as input, call on several well established and freely available genomic analysis tools to call SNPs and INDELs, and generate lists of the most likely causal mutations together with variant index plots to locate the mutation to a specific sequence position on a chromosome. The pipelines were validated with a known strawberry mutation before cloning the dek mutants, thereby enabling phenotypic analysis of large genomes by next‐generation sequencing.     

Scaling Up Single-Cell Proteomics

Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.     

页岩气开发对植被和土壤的影响研究进展

页岩气开发和生产过程影响水资源与水质、土地利用与植被覆盖、土壤侵蚀与土壤质量。综述了页岩气开发和生产过程可能存在的生态环境影响,并重点评价了国际上不同区域页岩气开发和生产对土地利用和植被覆被变化、景观破碎化的影响,以及对土壤侵蚀和土壤质量的影响。研究发现页岩气开发平台、运输道路和管线占用农田、牧场、森林,造成不同程度的景观破碎化,在坡地开发页岩气会导致土壤侵蚀与沉积风险增大。截止2015年末,在我国重庆涪陵焦石坝页岩气产建区55.8%的面积(146.56 km~2)存在土壤侵蚀和石漠化生态风险。我国页岩气开发区水基钻屑固化填埋未对周边土壤造成污染。建议页岩气开发设计应考虑占地、景观破碎化的影响,并及时开展页岩气开发暂时占地的复垦工作。     

Close encounters of the fatal kind: Landscape features associated with central mountain caribou mortalities

In western Canada, anthropogenic disturbances resulting from resource extraction activities are associated with habitat loss and altered predator–prey dynamics. These habitat changes are linked to increased predation risk and unsustainable mortality rates for caribou (Rangifer tarandus caribou). To inform effective habitat restoration, our goal was to examine whether specific linear disturbance features were associated with caribou predation in central mountain caribou ranges. We used predation‐caused caribou mortalities and caribou GPS‐collar data collected between 2008 and 2015 to assess caribou predation risk within and outside of protected areas at four spatio‐temporal scales: habitat use during the (a) 30 days, (b) 7 days, and (c) 24 hours prior to caribou being killed, and (d) characteristics at caribou kill site locations. Outside of protected areas, predation risk increased closer to pipelines, seismic lines, and streams. Within protected areas, predation risk increased closer to alpine habitat. Factors predicting predation risk differed among spatio‐temporal scales and linear feature types: predation risk increased closer to pipelines during the 30 and 7 days prior to caribou being killed and closer to seismic lines during the 30 days, 7 days, and 24 hours prior, but decreased closer to roads during the 30 days prior to being killed. By assessing habitat use prior to caribou being killed, we identified caribou predation risk factors that would not have been detected by analysis of kill site locations alone. These results provide further evidence that restoration of anthropogenic linear disturbance features should be an immediate priority for caribou recovery in central mountain caribou ranges.     

Microorganisms in Heat Supply Systems and Internal Corrosion of Steel Pipelines

In laboratory experiments with batch cultures of thermophilic microorganisms isolated from urban heat supply systems, the growth of sulfate-reducing, iron-oxidizing, and iron-reducing bacteria was found to accelerate the corrosion rate of the steel-3 plates used in pipelines. In the absence of bacteria and dissolved oxygen, minimal corrosion was determined. The aforementioned microorganisms, as well as sulfur-oxidizing bacteria, were found to be widespread in water and corrosion deposits in low-alloy steel pipelines (both delivery and return) of the Moscow heat networks, as well as in the corrosion deposits on the steel-3 plates in a testing unit supplied with the network water. The microorganisms were found in samples with a water pH ranging from 8.1 to 9.6 and a temperature lower than 90°C. Magnetite, lepidocrocite, goethite, and X-ray amorphous ferric oxide were the corrosion products identified on the steel-3 plates, as well as siderite, aragonite, and S⁰. The accumulation of corrosion deposits and variation in the total and local corrosion of the steel plates in a testing unit were considered in terms of the influence of microbial processes.