Are we ready to detect nematode diversity by next generation sequencing?

In a Technical Advance article, Porazinska et al. (2009, Molecular Ecology Resources, 9, 1439–1450) assessed next generation sequencing (NGS) as a method for metagenomic analysis of nematode diversity. We agree that NGS has great potential here. However, it is not an easy path to the successful implementation of NGS for environmental DNA analysis of nematodes. Here, we describe the method's limitations and discuss prospective research questions. For instance, only a few direct extraction kits are suitable for nematode DNA extraction from bulk samples without adaptation. They enable the analysis of extracellular nematode DNA. The most crucial and unresolved issue remains the limited availability of suitable primers.     

BioGPS and GXD: mouse gene expression data—the benefits and challenges of data integration

Mouse gene expression data are complex and voluminous. To maximize the utility of these data, they must be made readily accessible through databases, and those resources need to place the expression data in the larger biological context. Here we describe two community resources that approach these problems in different but complementary ways: BioGPS and the Mouse Gene Expression Database (GXD). BioGPS connects its large and homogeneous microarray gene expression reference data sets via plugins with a heterogeneous collection of external gene centric resources, thus casting a wide but loose net. GXD acquires different types of expression data from many sources and integrates these data tightly with other types of data in the Mouse Genome Informatics (MGI) resource, with a strong emphasis on consistency checks and manual curation. We describe and contrast the “loose” and “tight” data integration strategies employed by BioGPS and GXD, respectively, and discuss the challenges and benefits of data integration. BioGPS is freely available at http://biogps.org. GXD is freely available through the MGI web site (www.informatics.jax.org) or directly at www.informatics.jax.org/expression.shtml.     

Gridcast—a next generation broadcast infrastructure?

Gridcast is an R&D project investigating grid ideas and technologies in the broadcasting technical infrastructure. In this paper I discuss the business and technical issues in building infrastructures to support broadcasters and outline the structure of the Gridcast grid-based service oriented architecture for broadcasting playout support.

Performance comparison of SNP detection tools with illumina exome sequencing data—an assessment using both family pedigree information and sample-matched SNP array data

To apply exome-seq-derived variants in the clinical setting, there is an urgent need to identify the best variant caller(s) from a large collection of available options. We have used an Illumina exome-seq dataset as a benchmark, with two validation scenarios—family pedigree information and SNP array data for the same samples, permitting global high-throughput cross-validation, to evaluate the quality of SNP calls derived from several popular variant discovery tools from both the open-source and commercial communities using a set of designated quality metrics. To the best of our knowledge, this is the first large-scale performance comparison of exome-seq variant discovery tools using high-throughput validation with both Mendelian inheritance checking and SNP array data, which allows us to gain insights into the accuracy of SNP calling through such high-throughput validation in an unprecedented way, whereas the previously reported comparison studies have only assessed concordance of these tools without directly assessing the quality of the derived SNPs. More importantly, the main purpose of our study was to establish a reusable procedure that applies high-throughput validation to compare the quality of SNP discovery tools with a focus on exome-seq, which can be used to compare any forthcoming tool(s) of interest.     

REACH and LCA—methodological approaches and challenges

Purpose  

This paper discusses issues associated with the research question: What are the similarities and differences between the REACH and life cycle assessment (LCA) approaches, and how can synergies between these two approaches be exploited to achieve environmental improvements in a holistic perspective?     

crabs—A software program to generate curated reference databases for metabarcoding sequencing data

The measurement of biodiversity is an integral aspect of life science research. With the establishment of second- and third-generation sequencing technologies, an increasing amount of metabarcoding data is being generated as we seek to describe the extent and patterns of biodiversity in multiple contexts. The reliability and accuracy of taxonomically assigning metabarcoding sequencing data have been shown to be critically influenced by the quality and completeness of reference databases. Custom, curated, eukaryotic reference databases, however, are scarce, as are the software programs for generating them. Here, we present crabs (Creating Reference databases for Amplicon-Based Sequencing), a software package to create custom reference databases for metabarcoding studies. crabs includes tools to download sequences from multiple online repositories (i.e., NCBI, BOLD, EMBL, MitoFish), retrieve amplicon regions through in silico PCR analysis and pairwise global alignments, curate the database through multiple filtering parameters (e.g., dereplication, sequence length, sequence quality, unresolved taxonomy, inclusion/exclusion filter), export the reference database in multiple formats for immediate use in taxonomy assignment software, and investigate the reference database through implemented visualizations for diversity, primer efficiency, reference sequence length, database completeness and taxonomic resolution. crabs is a versatile tool for generating curated reference databases of user-specified genetic markers to aid taxonomy assignment from metabarcoding sequencing data. crabs can be installed via docker and is available for download as a conda package and via GitHub ( https://github.com/gjeunen/reference_database_creator ).     

VNTRseek—a computational tool to detect tandem repeat variants in high-throughput sequencing data

DNA tandem repeats (TRs) are ubiquitous genomic features which consist of two or more adjacent copies of an underlying pattern sequence. The copies may be identical or approximate. Variable number of tandem repeats or VNTRs are polymorphic TR loci in which the number of pattern copies is variable. In this paper we describe VNTRseek, our software for discovery of minisatellite VNTRs (pattern size ≥ 7 nucleotides) using whole genome sequencing data. VNTRseek maps sequencing reads to a set of reference TRs and then identifies putative VNTRs based on a discrepancy between the copy number of a reference and its mapped reads. VNTRseek was used to analyze the Watson and Khoisan genomes (454 technology) and two 1000 Genomes family trios (Illumina). In the Watson genome, we identified 752 VNTRs with pattern sizes ranging from 7 to 84 nt. In the Khoisan genome, we identified 2572 VNTRs with pattern sizes ranging from 7 to 105 nt. In the trios, we identified between 2660 and 3822 VNTRs per individual and found nearly 100% consistency with Mendelian inheritance. VNTRseek is, to the best of our knowledge, the first software for genome-wide detection of minisatellite VNTRs. It is available at http://orca.bu.edu/vntrseek/.     

Development of personal pollen information—the next generation of pollen information and a step forward for hay fever sufferers

Pollen allergies affect a large part of the European population and are considered likely to increase. User feedback indicates that there are difficulties in providing proper information and valid forecasts using traditional methods of aerobiology due to a variety of factors. Allergen content, pollen loads, and pollen allergy symptoms vary per region and year. The first steps in challenging such issues have already been undertaken. A personalized pollen-related symptom forecast is thought to be a possible answer. However, attempts made thus far have not led to an improvement in daily forecasting procedures. This study describes a model that was launched in 2013 in Austria to provide the first available personal pollen information. This system includes innovative forecast models using bi-hourly pollen data, traditional pollen forecasts based on historical data, meteorological data, and recent symptom data from the patient’s hayfever diary. Furthermore, it calculates the personal symptom load in real time, in particular, the entries of the previous 5 days, to classify users. The personal pollen information was made available in Austria on the Austrian pollen information website and via a mobile pollen application, described herein for the first time. It is supposed that the inclusion of personal symptoms will lead to major improvements in pollen information concerning hay fever sufferers.     

What is truth: Consensus and discordance in next‐generation phylogenetic analyses of Daucus

High‐throughput (next‐generation) DNA sequencing has removed barriers to data quantity and quality, and it has produced phylogenies with high statistical support. Such data are useful to address phylogenetic congruence among individual genes. Concatenated analyses of unlinked genes often produce well‐resolved phylogenetic trees with bootstrap support on major nodes at or approaching 100%, but they have been criticized for providing incorrect phylogenies for various reasons to include a history of hybridization, introgression, and incomplete lineage sorting. The present study compares next‐generation sequencing results of the same accessions of Daucus with different genomic regions, of which three have been reported before: (i) the entire plastid genome, (ii) 47 mitochondrial genes, and (iii) 94 conserved nuclear orthologs. Here, we report a fourth dataset, (iv) 564 895 nuclear SNPs. There are areas of discordance in all four results using the same accessions analyzed with maximum parsimony, maximum likelihood, and with the nuclear data species trees through a coalescent analysis. The nuclear results show significant areas of discordance that were unexpected, because these studies used the same DNA samples, the nuclear studies were generated from large and high‐quality datasets with the SNPs distributed on all nine linkage groups of Daucus carota, and the results were supported by high bootstrap values. These results raise questions concerning the best data and analytical methods to reconstruct and understand the “truth” of a phylogeny.     

Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling

Purpose

A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of “hotspots” in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level.

Methods

The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (Benoît and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator.

Results and discussion

The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots.

Conclusions

The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results.

     

Moist deciduous forest identification using temporal MODIS data — A comparative study using fuzzy based classifiers

The two soft fuzzy based classifiers, Possibilistic c-Means (PCM) approach and Noise Clustering (NC) were compared for the Moist Deciduous Forest (MDF) identification from MODIS temporal data. Seven date temporal MODIS data were used to identify MDF and temporal Advanced Wide Field Sensor (AWiFS) data was used as reference data for testing. Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index 2 (EVI2) were used to generate the temporal spectral index datasets for both the MODIS and AWiFS. The parameter weighting exponent m for PCM and resolution parameter δ for NC were optimized. Results show that the optimized value of m for MDF is 2.1, while δ value is 3.6 × 10⁴ for temporal MODIS data. For assessment of the accuracy AWiFS datasets were also optimized using entropy approach. The optimized dataset of AWiFS was then used for accuracy assessment of the soft classified outputs from MODIS using Fuzzy ERror Matrix (FERM). It was found from this study that, for PCM classifier the highest fuzzy overall accuracy of 97.44% was obtained using the SAVI for the temporal dataset ‘Five’ consisting to one scene of ‘Full greenness’, three scenes in ‘Intermediate frequency stage of Onset of Greenness (OG) and End of Senescence (ES) activity’ and the last image pertaining corresponds to the ‘Maximum frequency stage of OG activity’ as per phenology of MDF. Similarly, for NC classifier the highest fuzzy overall accuracy of 95.19% was obtained for the EVI2 with temporal dataset ‘Five’ consisting with two scene of ‘Full greenness’, two scenes in ‘Intermediate frequency stage of OG and ES activity’ and the last one corresponds to the ‘Maximum frequency stage of OG activity’as per phenology of MDF.     

Mass spectrometry-based sequencing of protein C-terminal peptide using α-carboxyl group-specific derivatization and COOH capturing

An approach to mass spectrometry (MS)-based sequence analysis of selectively enriched C-terminal peptide from protein is described. This approach employs a combination of the specific derivatization of α-carboxyl group (α-COOH), enzymatic proteolysis using endoproteinase GluC, and enrichment of C-terminal peptide through the use of COOH-capturing material. Highly selective derivatization of α-COOH was achieved by a combination of specific activation of α-COOH through oxazolone chemistry and amidation using 3-aminopropyltris-(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP-propylamine). This amine component was used to simplify fragmentation in tandem mass spectrometry (MS/MS) measurement, which facilitated manual sequence interpretation. The peptides produced after GluC digestion were then treated with a COOH scavenger to enrich the C-terminal peptide that is only devoid of COOH groups, and the obtained C-terminal peptide was readily sequenced by matrix-assisted laser desorption/ionization (MALDI)-MS/MS due to the TMPP mass tag.     

Mapping and analyzing China’s wetlands using MODIS time series data

Wetlands Ecology and Management - Gathering accurate information on wetland distribution and changes is of key importance for wetland management. However, wetland mapping using single-date imagery...     

Biodiesel production—current state of the art and challenges

Biodiesel is a clean-burning fuel produced from grease, vegetable oils, or animal fats. Biodiesel is produced by transesterification of oils with short-chain alcohols or by the esterification of fatty acids. The transesterification reaction consists of transforming triglycerides into fatty acid alkyl esters, in the presence of an alcohol, such as methanol or ethanol, and a catalyst, such as an alkali or acid, with glycerol as a byproduct. Because of diminishing petroleum reserves and the deleterious environmental consequences of exhaust gases from petroleum diesel, biodiesel has attracted attention during the past few years as a renewable and environmentally friendly fuel. Since biodiesel is made entirely from vegetable oil or animal fats, it is renewable and biodegradable. The majority of biodiesel today is produced by alkali-catalyzed transesterification with methanol, which results in a relatively short reaction time. However, the vegetable oil and alcohol must be substantially anhydrous and have a low free fatty acid content, because the presence of water or free fatty acid or both promotes soap formation. In this article, we examine different biodiesel sources (edible and nonedible), virgin oil versus waste oil, algae-based biodiesel that is gaining increasing importance, role of different catalysts including enzyme catalysts, and the current state-of-the-art in biodiesel production. JIMB 2008: BioEnergy—special issue.     

Whole exome sequencing of a single osteosarcoma case—integrative analysis with whole transcriptome RNA-seq data

Background

Osteosarcoma (OS) is a prevalent primary malignant bone tumour with unknown etiology. These highly metastasizing tumours are among the most frequent causes of cancer-related deaths. Thus, there is an urgent need for different markers, and with our study, we were aiming towards finding novel biomarkers for OS.

Methods

For that, we analysed the whole exome of the tumorous and non-tumour bone tissue from the same patient with OS applying next-generation sequencing. For data analysis, we used several softwares and combined the exome data with RNA-seq data from our previous study.

Results

In the tumour exome, we found wide genomic rearrangements, which should qualify as chromotripsis—we detected almost 3,000 somatic single nucleotide variants (SNVs) and small indels and more than 2,000 copy number variants (CNVs) in different chromosomes. Furthermore, the somatic changes seem to be associated to bone tumours, whereas germline mutations to cancer in general. We confirmed the previous findings that the most significant pathway involved in OS pathogenesis is probably the WNT/β-catenin signalling pathway. Also, the IGF1/IGF2 and IGF1R homodimer signalling and TP53 (including downstream tumour suppressor gene EI24) pathways may have a role. Additionally, the mucin family genes, especially MUC4 and cell cycle controlling gene CDC27 may be considered as potential biomarkers for OS.

Conclusions

The genes, in which the mutations were detected, may be considered as targets for finding biomarkers for OS. As the study is based on a single case and only DNA and RNA analysis, further confirmative studies are required.