Reverse Reads Research Articles

Evaluation of Rumen Microbes For Milk Fat Yield in Cattle Based on 16S rRNA Amplicon Metagenomic Sequencing

Background: Ruminal microbes support growth, lactation and maintenance of animals by providing energy and protein yielding nutrients. Metagenomics is the study of microbial communities directly in their natural environments such as soil, water, gut samples etc. The 16S rRNA gene sequencing is one of the basic genetic markers for the characterisation of prokaryote microorganism community. Current study exploited complex gut microbial communities present in cattle rumen based on variation in milk fat yield. Methods: The study was conducted in cattle reared at University Livestock Farm, Kerala Veterinary and Animal Sciences University. Twelve healthy cattle in the initial stages of lactation (less than three months) were selected and divided into two groups of six each based on high and low milk fat composition. Rumen samples were collected and DNA was isolated. Amplicons of 16S rRNA has been generated from metagenomic DNA targeting V3 and V4 hyper variable regions. Qiime2 was used for analysis for 16S rRNA amplicon sequencing. Result: Results revealed that a total of 38,28,241 sequences were obtained after demultiplexing. Majority of sequence length ranged around 295 to 301 nucleotides for forward reads and 216 to 300 nucleotides for reverse reads. After denoising, a total of 6,62,498 sequences were obtained. Clustering (de-replication) produced 711 unique sequence features. In kingdom level of taxonomy, for bacterial kingdom, there was no significant difference in abundance of organisms for high milk fat yield and low milk fat yield cows. For archaea, significant difference observed between abundance of high milk fat and low milk fat groups at five per cent level in the current study.

Measurements of soil protist richness and community composition are influenced by primer pair, annealing temperature, and bioinformatics choices.

Protists are a diverse and understudied group of microbial eukaryotic organisms especially in terrestrial environments. Advances in molecular methods are increasing our understanding of the distribution and functions of these creatures; however, there is a vast array of choices researchers make including barcoding genes, primer pairs, PCR settings, and bioinformatic options that can impact the outcome of protist community surveys. Here, we tested four commonly used primer pairs targeting the V4 and V9 regions of the 18S rRNA gene using different PCR annealing temperatures and processed the sequences with different bioinformatic parameters in 10 diverse soils to evaluate how primer pair, amplification parameters, and bioinformatic choices influence the composition and richness of protist and non-protist taxa using Illumina sequencing. Our results showed that annealing temperature influenced sequencing depth and protist taxon richness for most primer pairs, and that merging forward and reverse sequencing reads for the V4 primer pairs dramatically reduced the number of sequences and taxon richness of protists. The data sets of primers that targeted the same 18S rRNA gene region (e.g., V4 or V9) had similar protist community compositions; however, data sets from primers targeting the V4 18S rRNA gene region detected a greater number of protist taxa compared to those prepared with primers targeting the V9 18S rRNA region. There was limited overlap of protist taxa between data sets targeting the two different gene regions (80/549 taxa). Together, we show that laboratory and bioinformatic choices can substantially affect the results and conclusions about protist diversity and community composition using metabarcoding.IMPORTANCEEcosystem functioning is driven by the activity and interactions of the microbial community, in both aquatic and terrestrial environments. Protists are a group of highly diverse, mostly unicellular microbes whose identity and roles in terrestrial ecosystem ecology have been largely ignored until recently. This study highlights the importance of choices researchers make, such as primer pair, on the results and conclusions about protist diversity and community composition in soils. In order to better understand the roles protist taxa play in terrestrial ecosystems, biases in methodological and analytical choices should be understood and acknowledged.

Analysis of DNA Barcodes Using DNA Subway.

DNA Subway makes bioinformatic analysis of DNA barcodes classroom friendly, eliminating the need for software installations or command line tools. Subway bundles research-grade bioinformatics software into workflows with an easy-to-use interface. This chapter covers DNA Subway's DNA barcoding analysis workflow (Blue Line) starting with one or more Sanger sequence reads. During analysis, users can view trace files and sequence quality, pair and align forward and reverse reads, create and trim consensus sequences, perform BLAST searches, select reference data, align multiple sequences, and compute phylogenetic trees. High-quality sequences with the required metadata can also be submitted as barcode sequences to NCBI GenBank.

Transferable IncX3 plasmid harboring blaNDM-1, bleMBL, and aph(3’)-VI genes from Klebsiella pneumoniae conferring phenotypic carbapenem resistance in E. coli

BackgroundThe dissemination of carbapenem resistance via carbapenemases, such as the metallo-β-lactamase NDM, among Enterobacterales poses a public health threat. The aim of this study was to characterize a plasmid carrying the blaNDM-1 gene, which was extracted from a clinical Klebsiella pneumoniae uropathogen from an Egyptian patient suffering from a urinary tract infection.Methods and resultsThe recovered plasmid was transformed into competent E. coli DH5α which acquired phenotypic resistance to cefoxitin, ceftazidime, and ampicillin/sulbactam, and intermediate sensitivity to ceftriaxone and imipenem (a carbapenem). Whole plasmid sequencing was performed on the extracted plasmid using the DNBSEQ™ platform. The obtained forward and reverse reads were assembled into contigs using the PRINSEQ and PLACNETw web tools. The obtained contigs were uploaded to PlasmidFinder and ResFinder for in silico plasmid typing and detection of antimicrobial resistance genes, respectively. The final consensus sequence was obtained using the Staden Package software. The plasmid (pNDMKP37, NCBI accession OK623716.1) was typed as an IncX3 plasmid with a size of 46,160 bp and harbored the antibiotic resistance genes blaNDM-1, bleMBL, and aph(3’)-VI. The plasmid also carried mobile genetic elements involved in the dissemination of antimicrobial resistance including insertion sequences IS30, IS630, and IS26.ConclusionsThis is Egypt’s first report of a transmissible plasmid co-harboring blaNDM-1 and aph(3’)-VI genes. Moreover, the respective plasmid is of great medical concern as it has caused the horizontal transmission of multidrug-resistant phenotypes to the transformant. Therefore, new guidelines should be implemented for the rational use of broad-spectrum antibiotics, particularly carbapenems.

EP320: AI-based method to estimate the probability of a variant being an artifact

Next Generation Sequencing is progressively becoming the leading DNA and RNA sequencing technology. It allows for massive parallel sequencing of DNA/RNA sequences in a relatively short period of time and reduced cost when compared to Sanger sequencing, producing large quantities of short sequence reads. However, NGS technologies introduce a certain amount of artifacts that are not always removed by the bioinformatics pipeline at the secondary data analysis. These artifacts can complicate the analysis of results and if they remain undetected can be a source of false positives at the variant interpretation stage. We developed an automated method based on artificial intelligence to automatically detect these artifacts so that they can be removed early in the variant interpretation pipeline avoiding false positives and saving genetic analyst precious time. A total of 200 variants were extracted from a set of 11 samples of patients coming from La Fe Hospital in Spain. These variants were a combination of real sequence variants and artifacts that were previously classified by two different human expert geneticists. A total of 80 variants were classified as real sequence variants with agreement between the two geneticists. 78 were classified as artifacts, and 42 were declared unknown by the geneticists, or a consensus about their classification was not reached. With the selected variants and artifacts a training set of 158 variants was created to feed an artificial intelligence system. For each variant, the elements used as an input of the system were : 1) sequencing depth 2) total number of reference reads 3) total number of alternative reads 4) total forward reference reads 5) total reverse reference reads 6) total forward alternative reads 7) total reverse alternative reads 8) whether the variant is a section of repetition. The system was programmed using Python programming language and NumPy and samtools libraries. In order to extract the required parameters, the system required access to both .VCF and .BAM files. After an initial training period of the system with several artificial intelligence engines, a logistic regression classifier was selected as the best performing candidate. The proposed method reported concordance with human classification in 97,7% of cases in a sample of 200 variants classified in the laboratory. Precision reached 94,11% with a recall of 100%. We presented a novel method for artefact detection in NGS tertiary analysis. The proposed methodology could be considered as a complement to existing ACMG guidelines in order to establish the validity of variant interpretation results. The method can save time and steps necessary to validate variants before being included in clinical reports. The method can be improved with the addition of new input parameters, such a nucleotides quality scores as well as cohort analysis of samples coming from the same sequencer batch.

Environmental identification of arbuscular mycorrhizal fungi using the LSU rDNA gene region: an expanded database and improved pipeline

Arbuscular mycorrhizal fungi (AMF; Glomeromycota) are difficult to culture; therefore, establishing a robust amplicon-based approach to taxa identification is imperative to describe AMF diversity. Further, due to low and biased sampling of AMF taxa, molecular databases do not represent the breadth of AMF diversity, making database matching approaches suboptimal. Therefore, a full description of AMF diversity requires a tool to determine sequence-based placement in the Glomeromycota clade. Nonetheless, commonly used gene regions, including the SSU and ITS, do not enable reliable phylogenetic placement. Here, we present an improved database and pipeline for the phylogenetic determination of AMF using amplicons from the large subunit (LSU) rRNA gene. We improve our database and backbone tree by including additional outgroup sequences. We also improve an existing bioinformatics pipeline by aligning forward and reverse reads separately, using a universal alignment for all tree building, and implementing a BLAST screening prior to tree building to remove non-homologous sequences. Finally, we present a script to extract AMF belonging to 11 major families as well as an amplicon sequencing variant (ASV) version of our pipeline. We test the utility of the pipeline by testing the placement of known AMF, known non-AMF, and Acaulospora sp. spore sequences. This work represents the most comprehensive database and pipeline for phylogenetic placement of AMF LSU amplicon sequences within the Glomeromycota clade.

The relationship between equine pastern dermatitis, meteorological factors, and the skin microbiota.

BackgroundEquine pastern dermatitis (EPD) is a multifactorial syndrome, with prolonged exposure to moisture assumed to be a predisposing or primary factor.Hypothesis/ObjectivesTo examine the course of EPD lesion severity, changes in bacterial skin microbiota, and the influence of meteorological factors.AnimalsProspective, longitudinal cohort study over a one‐year period, with six Franches‐Montagnes stallions, four affected by EPD and two unaffected, that were kept under the same conditions.Methods and materialsPasterns were scored for lesion severity and sampled once a month for 12 consecutive months. Lesion severity, the skin microbiota and meteorological factors were examined for associations.ResultsEPD lesions tended to worsen in autumn and at the beginning of spring. The relationship between lesion severity and the meteorological factor precipitation was not clearly evident; high scores were preceded by both low or high rates of precipitation. Microbiota in affected pasterns appeared to have experienced a reduction in alpha diversity. Beta diversity analyses demonstrated that bacterial community structures were altered in affected versus unaffected pasterns, and that alterations were more pronounced with higher EPD scores (P = 0.005). Meteorological factors also had considerable influences on the bacterial composition, whereby these influences appeared to be more marked in the affected pasterns (P = 0.001, F = 3.19) than in unaffected ones (P = 0.005, F = 1.83).Conclusions and clinical relevanceOur study provides preliminary observations of the relationships between lesion severity, meteorological factors and cutaneous bacteria. The population was too small to draw firm conclusions, and further studies on environmental factors and the involvement of bacteria in this multifactorial disease are needed.

A comprehensive evaluation of single-end sequencing data analyses for environmental microbiome research.

Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3' end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low-quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

VGEA: an RNA viral assembly toolkit.

Next generation sequencing (NGS)-based studies have vastly increased our understanding of viral diversity. Viral sequence data obtained from NGS experiments are a rich source of information, these data can be used to study their epidemiology, evolution, transmission patterns, and can also inform drug and vaccine design. Viral genomes, however, represent a great challenge to bioinformatics due to their high mutation rate and forming quasispecies in the same infected host, bringing about the need to implement advanced bioinformatics tools to assemble consensus genomes well-representative of the viral population circulating in individual patients. Many tools have been developed to preprocess sequencing reads, carry-out de novo or reference-assisted assembly of viral genomes and assess the quality of the genomes obtained. Most of these tools however exist as standalone workflows and usually require huge computational resources. Here we present (Viral Genomes Easily Analyzed), a Snakemake workflow for analyzing RNA viral genomes. VGEA enables users to map sequencing reads to the human genome to remove human contaminants, split bam files into forward and reverse reads, carry out de novo assembly of forward and reverse reads to generate contigs, pre-process reads for quality and contamination, map reads to a reference tailored to the sample using corrected contigs supplemented by the user’s choice of reference sequences and evaluate/compare genome assemblies. We designed a project with the aim of creating a flexible, easy-to-use and all-in-one pipeline from existing/stand-alone bioinformatics tools for viral genome analysis that can be deployed on a personal computer. VGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: fastp (Chen et al., 2018) for read trimming and read-level quality control, BWA (Li & Durbin, 2009) for mapping sequencing reads to the human reference genome, SAMtools (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, IVA (Hunt et al., 2015) for de novo assembly to generate contigs, shiver (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user’s choice of existing reference sequences, SeqKit (Shen et al., 2016) for cleaning shiver assembly for QUAST, QUAST (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies and MultiQC (Ewels et al., 2016) for aggregation of the results from fastp, BWA and QUAST. Our pipeline was successfully tested and validated with SARS-CoV-2 (n = 20), HIV-1 (n = 20) and Lassa Virus (n = 20) datasets all of which have been made publicly available. VGEA is freely available on GitHub at: https://github.com/pauloluniyi/VGEA under the GNU General Public License.

SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data

BackgroundThere is currently no method to precisely measure the errors that occur in the sequencing instrument/sequencer, which is critical for next-generation sequencing applications aimed at discovering the genetic makeup of heterogeneous cellular populations.ResultsWe propose a novel computational method, SequencErr, to address this challenge by measuring the base correspondence between overlapping regions in forward and reverse reads. An analysis of 3777 public datasets from 75 research institutions in 18 countries revealed the sequencer error rate to be ~ 10 per million (pm) and 1.4% of sequencers and 2.7% of flow cells have error rates > 100 pm. At the flow cell level, error rates are elevated in the bottom surfaces and > 90% of HiSeq and NovaSeq flow cells have at least one outlier error-prone tile. By sequencing a common DNA library on different sequencers, we demonstrate that sequencers with high error rates have reduced overall sequencing accuracy, and removal of outlier error-prone tiles improves sequencing accuracy. We demonstrate that SequencErr can reveal novel insights relative to the popular quality control method FastQC and achieve a 10-fold lower error rate than popular error correction methods including Lighter and Musket.ConclusionsOur study reveals novel insights into the nature of DNA sequencing errors incurred on DNA sequencers. Our method can be used to assess, calibrate, and monitor sequencer accuracy, and to computationally suppress sequencer errors in existing datasets.

First Record of Fungi Isolated from Thrips tabaci Lindeman on Garlic (Ilocos White) in Batac, Philippines

The study aimed to isolate, and identify fungi associated with live adult female Thrips tabaci Lindeman (Thysanoptera: Thripidae) from garlic (Ilocos White) in Tubug Experimental Area of Mariano Marcos State University in Batac, Ilocos Norte, Philippines. Thrips were collected randomly from garlic (Ilocos White) plants within 200 m2 area in one garlic field of about 400 m2 using the tapping/beating method. Thrips were sorted and examined under the microscope. For the isolation of fungi, 20 live adult female thrips were selected from the collection consisting of females and larvae. Standard isolation of fungi from thrips was conducted and pure cultures of fungal isolates were maintained on PDA (potato dextrose agar) slants. Fungal isolates were processed for DNA extraction, PCR (polymerase chain reaction) amplification, and gel electrophoresis. Forward and reverse reads were paired to generate a consensus sequence, which was used in the Basic Local Alignment Search Tool (BLAST) queries to determine taxonomic identity. Out of the 10 fungal isolates, nine were identified as Fusarium proliferatum while one is Aspergillus candidus based on ITS (internal transcribed spacer) gene sequence. They both belong to Class Ascomycota. F. proliferatum on PDA shows varying characteristics of its mycelial growth from being scanty to fluffy, off-white to light yellow, brown, or purple while A. candidatus exhibits the entire margin and bluish-green pigmentation with a fertile layer composed of catenate conidia formed from the vesicle of the conidiophore. F. proliferatum was dominant among the isolates. The manner of isolating them from Thrips tabaci proved that they are endophytes living within them, and these are new records in the Philippines.

Characterization of background noise in MiSeq MPS data when sequencing human mitochondrial DNA from various sample sources and library preparation methods

Improved resolution of massively parallel sequencing (MPS) allows for the characterization of mitochondrial (mt) DNA heteroplasmy to levels previously unattainable with traditional sequencing approaches. An essential criterion for the reporting of heteroplasmy is the ability of the MPS method to distinguish minor sequence variants (MSVs) from system noise, or error. Therefore, an assessment of the background noise in the MPS method is desirable to identify the point at which reliable data can be reported. Substitution and sequence specific error (SSE) was evaluated for a variety of sample types and two library preparations. Substitution error rates ranged from 0.18 to 0.49 per 100 nucleotides with C positions generally having the highest rate of misincorporation. Comparison of error rates across sample types indicated a significant increase for samples with damaged DNA. The positions of error were varied across datasets (pairwise concordance 0–68%), but had greater consistency within the damaged samples (80–96%). The most commonly observed motif preceding error in forward reads was CCG, while GGT was most common in reverse reads, both consistent with previous findings. The findings illustrate that for datasets containing samples with damaged DNA, reporting thresholds for heteroplasmy may have to be modified and individual sites with error levels exceeding thresholds should be scrutinized. Collectively, the shifting error profiles observed across the various sample types and library preparation methods demonstrates the need for an assessment of error under these varying circumstances. Characterization of the applicable background noise will help to ensure that thresholds are reliably set for detection of true MSVs.

Stacks 2: Analytical methods for paired-end sequencing improve RADseq-based population genomics.

For half a century population genetics studies have put type II restriction endonucleases to work. Now, coupled with massively-parallel, short-read sequencing, the family of RAD protocols that wields these enzymes has generated vast genetic knowledge from the natural world. Here, we describe the first software natively capable of using paired-end sequencing to derive short contigs from de novo RAD data. Stacks version 2 employs a de Bruijn graph assembler to build and connect contigs from forward and reverse reads for each de novo RAD locus, which it then uses as a reference for read alignments. The new architecture allows all the individuals in a metapopulation to be considered at the same time as each RAD locus is processed. This enables a Bayesian genotype caller to provide precise SNPs, and a robust algorithm to phase those SNPs into long haplotypes, generating RAD loci that are 400-800bp in length. To prove its recall and precision, we tested the software with simulated data and compared reference-aligned and de novo analyses of three empirical data sets. Our study shows that the latest version of Stacks is highly accurate and outperforms other software in assembling and genotyping paired-end de novo data sets.

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing.

The human gut is colonized by trillions of bacteria that support physiologic functions such as food metabolism, energy harvesting, and regulation of the immune system. Perturbation of the healthy gut microbiome has been suggested to play a role in the development of inflammatory diseases, including multiple sclerosis (MS). Environmental and genetic factors can influence the composition of the microbiome; therefore, identification of microbial communities linked with a disease phenotype has become the first step towards defining the microbiome's role in health and disease. Use of 16S rRNA metagenomic sequencing for profiling bacterial community has helped in advancing microbiome research. Despite its wide use, there is no uniform protocol for 16S rRNA-based taxonomic profiling analysis. Another limitation is the low resolution of taxonomic assignment due to technical difficulties such as smaller sequencing reads, as well as use of only forward (R1) reads in the final analysis due to low quality of reverse (R2) reads. There is need for a simplified method with high resolution to characterize bacterial diversity in a given biospecimen. Advancements in sequencing technology with the ability to sequence longer reads at high resolution have helped to overcome some of these challenges. Present sequencing technology combined with a publicly available metagenomic analysis pipeline such as R-based Divisive Amplicon Denoising Algorithm-2 (DADA2) has helped advance microbial profiling at high resolution, as DADA2 can assign sequence at the genus and species levels. Described here is a guide for performing bacterial profiling using two-step amplification of the V3-V4 region of the 16S rRNA gene, followed by analysis using freely available analysis tools (i.e., DADA2, Phyloseq, and METAGENassist). It is believed that this simple and complete workflow will serve as an excellent tool for researchers interested in performing microbiome profiling studies.

Root Microbiota in Primary and Secondary Apical Periodontitis.

Apical periodontitis is an inflammatory disease of the dental periradicular tissues triggered by bacteria colonizing necrotic root canals. Primary apical periodontitis results from the microbial colonization of necrotic pulp tissues. Secondary apical periodontitis results from a persistent infection of incorrectly treated root canals. The aim of this study was to characterize the microbiota present in primary and secondary intraradicular infections associated with apical periodontitis using 16S rRNA gene amplicon sequencing. Teeth exhibiting apical periodontitis with or without root canal treatment were extracted after informed consent. From each tooth, the intraradicular content as well as a dentin sample (control) were collected and subjected to DNA extraction. PCR amplicons of the V3–V4 region of the bacterial 16S rRNA gene were pooled and sequenced (2 × 300) on an Illumina MiSeq instrument. The bioinformatics analysis pipeline included quality filtering, merging of forward and reverse reads, clustering of reads into operational taxonomic units (OTUs), removal of putative contaminant OTUs and assigning taxonomy. The most prevalent and abundant OTU in both dentin and root canal samples was assigned to anaerobic bacterium Fusobacterium nucleatum. Multivariate analysis showed clustering of microbiota by sample type (dentin vs. intraradicular content) and, in root canals, by pathology (primary vs. secondary infection). The proportions of Enterococcus faecalis and F. nucleatum were, respectively, higher and lower when comparing secondary to primary infected root canals. Co-occurrence network analysis provided evidence of microbial interactions specific to the infection type. The identification of bacterial taxa differentially abundant in primary and secondary intraradicular infections may provide the basis for targeted therapeutic approaches aimed at reducing the incidence of apical periodontitis.

Corrigendum.

Gaspari T, Spizzo I, Liu HB, et al. (2018) Dapagliflozin attenuates human vascular endothelial cell activation and induces vasorelaxation: A potential mechanism for inhibition of atherogenesis. Diabetes & Vascular Disease Research 2018; 15(1): 64-73. DOI: 10.1177/1479164117733626 The authors have highlighted a typographical error which exists in the NFκB reverse primer sequence outlined in the paper. The published NFκB reverse primer sequence reads 5'-TGT-CGT-GCT-CCA-CAC-AGC-CAG-GT-3'. The correct sequence for the NFκB reverse primer sequence should read 5'-TGT-CGT-GCT-CCA-CAG-CCA-GGT-3'. The correct NFκB primer sequence was the sequence utilised in production of the NFκB reverse primer used in all PCR experiments undertaken in this study and thus does not impact on the validity of the PCR results.

Fast and Simple Analysis of MiSeq Amplicon Sequencing Data with MetaAmp.

Microbial community profiling by barcoded 16S rRNA gene amplicon sequencing currently has many applications in microbial ecology. The low costs of the parallel sequencing of multiplexed samples, combined with the relative ease of data processing and interpretation (compared to shotgun metagenomes) have made this an entry-level approach. Here we present the MetaAmp pipeline for processing of SSU rRNA gene and other non-coding or protein-coding amplicon sequencing data by investigators that are inexperienced with bioinformatics procedures. It accepts single-end or paired-end sequences in fasta or fastq format from various sequencing platforms. It includes read quality control, and merging of forward and reverse reads of paired-end reads. It makes use of UPARSE, Mothur, and the SILVA database for clustering, removal of chimeric reads, taxonomic classification, and generation of diversity metrics. The pipeline has been validated with a mock community of known composition. MetaAmp provides a convenient web interface as well as command line interface. It is freely available at: http://ebg.ucalgary.ca/metaamp. Since its launch 2 years ago, MetaAmp has been used >2,800 times, by many users worldwide.

The 'Next-in-Cml' Study: A Prospective Multicenter Study of Deep Sequencing of the BCR-ABL1 Kinase Domain in Philadelphia Chromosome-Positive Patients with Non-Optimal Responses to Tyrosine Kinase Inhibitor Therapy

The 'Next-in-Cml' Study: A Prospective Multicenter Study of Deep Sequencing of the BCR-ABL1 Kinase Domain in Philadelphia Chromosome-Positive Patients with Non-Optimal Responses to Tyrosine Kinase Inhibitor Therapy

SeqPurge: highly-sensitive adapter trimming for paired-end NGS data.

BackgroundTrimming of adapter sequences from short read data is a common preprocessing step during NGS data analysis. When performing paired-end sequencing, the overlap between forward and reverse read can be used to identify excess adapter sequences. This is exploited by several previously published adapter trimming tools. However, our evaluation on amplicon-based data shows that most of the current tools are not able to remove all adapter sequences and that adapter contamination may even lead to spurious variant calls.ResultsHere we present SeqPurge (https://github.com/imgag/ngs-bits), a highly-sensitive adapter trimmer that uses a probabilistic approach to detect the overlap between forward and reverse reads of Illumina sequencing data. SeqPurge can detect very short adapter sequences, even if only one base long. Compared to other adapter trimmers specifically designed for paired-end data, we found that SeqPurge achieves a higher sensitivity. The number of remaining adapter bases after trimming is reduced by up to 90 %, depending on the compared tool. In simulations with different error rates, we found that SeqPurge is also the most error-tolerant adapter trimmer in the comparison.ConclusionSeqPurge achieves a very high sensitivity and a high error-tolerance, combined with a specificity and runtime that are comparable to other state-of-the-art adapter trimmers. The very good adapter trimming performance, complemented with additional features such as quality-based trimming and basic quality control, makes SeqPurge an excellent choice for the pre-processing of paired-end NGS data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1069-7) contains supplementary material, which is available to authorized users.

AptaPLEX – A dedicated, multithreaded demultiplexer for HT-SELEX data

Aptamers, short and synthetic RNA/DNA molecules binding distinct targets with high affinity and specificity, are identified via Systematic Evolution of Ligands by Exponential Enrichment (SELEX), an in vitro procedure that, starting from a pool of random ssDNA/RNA sequences, selects sequences by amplifying target-affine species through a series of selection cycles. This versatile protocol has recently been combined with high throughput sequencing, allowing arbitrary stages of the selection to be sequenced and analyzed in silico. As a prerequisite, these data require extensive preprocessing by means of quality controls, error correction and demultiplexing, all while taking into account the specific design of aptamers. Existing solutions addressing this task are currently present only as integrated components in larger pipelines, limiting their applicability in independent software solutions.Here we present AptaPLEX, a standalone and platform independent demultiplexer specifically designed for HT-SELEX data. Given the multiplexed data from one or multiple HT-SELEX experiments, AptaPLEX extracts and restores aptamers into the original selection cycles by identifying the barcode and primer regions in each read. AptaPLEX is capable of fuzzy matching for both the barcode and primers, and automatically corrects mismatches between forward and reverse reads for paired-end data. Our software provides a rich set of additional features and can easily be integrated into existing analysis automation pipelines on multiple platforms ranging from desktop machines to cloud based solutions.