KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

MetaBinG2: a fast and accurate metagenomic sequence classification system for samples with many unknown organisms

Background

Many methods have been developed for metagenomic sequence classification, and most of them depend heavily on genome sequences of the known organisms. A large portion of sequencing sequences may be classified as unknown, which greatly impairs our understanding of the whole sample.

Result

Here we present MetaBinG2, a fast method for metagenomic sequence classification, especially for samples with a large number of unknown organisms. MetaBinG2 is based on sequence composition, and uses GPUs to accelerate its speed. A million 100 bp Illumina sequences can be classified in about 1 min on a computer with one GPU card. We evaluated MetaBinG2 by comparing it to multiple popular existing methods. We then applied MetaBinG2 to the dataset of MetaSUB Inter-City Challenge provided by CAMDA data analysis contest and compared community composition structures for environmental samples from different public places across cities.

Conclusion

Compared to existing methods, MetaBinG2 is fast and accurate, especially for those samples with significant proportions of unknown organisms.

Reviewers

This article was reviewed by Drs. Eran Elhaik, Nicolas Rascovan, and Serghei Mangul.

     

COGcollator: a web server for analysis of distant relationships between homologous protein families

Background

The Clusters of Orthologous Groups (COGs) of proteins systematize evolutionary related proteins into specific groups with similar functions. However, the available databases do not provide means to assess the extent of similarity between the COGs.

Aim

We intended to provide a method for identification and visualization of evolutionary relationships between the COGs, as well as a respective web server.

Results

Here we introduce the COGcollator, a web tool for identification of evolutionarily related COGs and their further analysis. We demonstrate the utility of this tool by identifying the COGs that contain distant homologs of (i) the catalytic subunit of bacterial rotary membrane ATP synthases and (ii) the DNA/RNA helicases of the superfamily 1.

Reviewers

This article was reviewed by Drs. Igor N. Berezovsky, Igor Zhulin and Yuri Wolf.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

HAlign-II: efficient ultra-large multiple sequence alignment and phylogenetic tree reconstruction with distributed and parallel computing

Background

Multiple sequence alignment (MSA) plays a key role in biological sequence analyses, especially in phylogenetic tree construction. Extreme increase in next-generation sequencing results in shortage of efficient ultra-large biological sequence alignment approaches for coping with different sequence types.

Methods

Distributed and parallel computing represents a crucial technique for accelerating ultra-large (e.g. files more than 1 GB) sequence analyses. Based on HAlign and Spark distributed computing system, we implement a highly cost-efficient and time-efficient HAlign-II tool to address ultra-large multiple biological sequence alignment and phylogenetic tree construction.

Results

The experiments in the DNA and protein large scale data sets, which are more than 1GB files, showed that HAlign II could save time and space. It outperformed the current software tools. HAlign-II can efficiently carry out MSA and construct phylogenetic trees with ultra-large numbers of biological sequences. HAlign-II shows extremely high memory efficiency and scales well with increases in computing resource.

Conclusions

THAlign-II provides a user-friendly web server based on our distributed computing infrastructure. HAlign-II with open-source codes and datasets was established at http://lab.malab.cn/soft/halign.

     

A streamlined method for analysing genome-wide DNA methylation patterns from low amounts of FFPE DNA

Background

Formalin fixed paraffin embedded (FFPE) tumor samples are a major source of DNA from patients in cancer research. However, FFPE is a challenging material to work with due to macromolecular fragmentation and nucleic acid crosslinking. FFPE tissue particularly possesses challenges for methylation analysis and for preparing sequencing-based libraries relying on bisulfite conversion. Successful bisulfite conversion is a key requirement for sequencing-based methylation analysis.

Methods

Here we describe a complete and streamlined workflow for preparing next generation sequencing libraries for methylation analysis from FFPE tissues. This includes, counting cells from FFPE blocks and extracting DNA from FFPE slides, testing bisulfite conversion efficiency with a polymerase chain reaction (PCR) based test, preparing reduced representation bisulfite sequencing libraries and massively parallel sequencing.

Results

The main features and advantages of this protocol are:

• An optimized method for extracting good quality DNA from FFPE tissues.
• An efficient bisulfite conversion and next generation sequencing library preparation protocol that uses 50 ng DNA from FFPE tissue.
• Incorporation of a PCR-based test to assess bisulfite conversion efficiency prior to sequencing.

Conclusions

We provide a complete workflow and an integrated protocol for performing DNA methylation analysis at the genome-scale and we believe this will facilitate clinical epigenetic research that involves the use of FFPE tissue.

     

Identification of usual interstitial pneumonia pattern using RNA-Seq and machine learning: challenges and solutions

Background

We developed a classifier using RNA sequencing data that identifies the usual interstitial pneumonia (UIP) pattern for the diagnosis of idiopathic pulmonary fibrosis. We addressed significant challenges, including limited sample size, biological and technical sample heterogeneity, and reagent and assay batch effects.

Results

We identified inter- and intra-patient heterogeneity, particularly within the non-UIP group. The models classified UIP on transbronchial biopsy samples with a receiver-operating characteristic area under the curve of ~?0.9 in cross-validation. Using in silico mixed samples in training, we prospectively defined a decision boundary to optimize specificity at ≥85%. The penalized logistic regression model showed greater reproducibility across technical replicates and was chosen as the final model. The final model showed sensitivity of 70% and specificity of 88% in the test set.

Conclusions

We demonstrated that the suggested methodologies appropriately addressed challenges of the sample size, disease heterogeneity and technical batch effects and developed a highly accurate and robust classifier leveraging RNA sequencing for the classification of UIP.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Comprehensive annotation of microRNA expression profiles

Background

MicroRNAs (miRNAs) regulate many biological processes by post-translational gene silencing. Analysis of miRNA expression profiles is a reliable method for investigating particular biological processes due to the stability of miRNA and the development of advanced sequencing methods. However, this approach is limited by the broad specificity of miRNAs, which may target several mRNAs.

Result

In this study, we developed a method for comprehensive annotation of miRNA array or deep sequencing data for investigation of cellular biological effects. Using this method, the specific pathways and biological processes involved in Alzheimer’s disease were predicted with high correlation in four independent samples. Furthermore, this method was validated for evaluation of cadmium telluride (CdTe) nanomaterial cytotoxicity. As a result, apoptosis pathways were selected as the top pathways associated with CdTe nanoparticle exposure, which is consistent with previous studies.

Conclusions

Our findings contribute to the validation of miRNA microarray or deep sequencing results for early diagnosis of disease and evaluation of the biological safety of new materials and drugs.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Mapping the landscape of tandem repeat variability by targeted long read single molecule sequencing in familial X-linked intellectual disability

Background

The etiology of more than half of all patients with X-linked intellectual disability remains elusive, despite array-based comparative genomic hybridization, whole exome or genome sequencing. Since short read massive parallel sequencing approaches do not allow the detection of larger tandem repeat expansions, we hypothesized that such expansions could be a hidden cause of X-linked intellectual disability.

Methods

We selectively captured over 1800 tandem repeats on the X chromosome and characterized them by long read single molecule sequencing in 3 families with idiopathic X-linked intellectual disability.

Results

In male DNA samples, full tandem repeat length sequences were obtained for 88–93% of the targets and up to 99.6% of the repeats with a moderate guanine-cytosine content. Read length and analysis pipeline allow to detect cases of >?900?bp tandem repeat expansion. In one family, one repeat expansion co-occurs with down-regulation of the neighboring MIR222 gene. This gene has previously been implicated in intellectual disability and is apparently linked to FMR1 and NEFH overexpression associated with neurological disorders.

Conclusions

This study demonstrates the power of single molecule sequencing to measure tandem repeat lengths and detect expansions, and suggests that tandem repeat mutations may be a hidden cause of X-linked intellectual disability.

     

Identification of trunk mutations in gastric carcinoma: a case study

Background

Intratumor heterogeneity (ITH) poses an urgent challenge for cancer precision medicine because it can cause drug resistance against cancer target therapy and immunotherapy. The search for trunk mutations that are present in all cancer cells is therefore critical for each patient.

Case presentation

In this study, we aimed to evaluate the efficiency of multiregional sequencing for the identification of trunk mutations present in all regions of a tumor as a case study. We applied multiregional whole-exome sequencing (WES) to investigate the genetic heterogeneity and homogeneity of a case of gastric carcinoma. Approximately 83% of common missense mutations present in two samples and approximately 89% of common missense mutations present in three samples were trunk mutations. Notably, trunk mutations appeared to have higher variant allele frequencies (VAFs) than non-trunk mutations.

Conclusions

Our results indicate that small-scale multiregional sampling and subsequent screening of low VAF somatic mutations might be a cost-effective strategy for identifying the majority of trunk mutations in gastric carcinoma.

     

Variation of the microbiota and metabolome along the canine gastrointestinal tract

Introduction

The fecal microbiota are relevant to the health and disease of many species. The importance of the fecal metabolome has more recently been appreciated, but our knowledge of the microbiota and metabolome at other sites along the gastrointestinal tract remains deficient.

Objective

To analyze the gastrointestinal microbiota and metabolome of healthy domestic dogs at four anatomical sites.

Methods

Samples of the duodenal, ileal, colonic, and rectal contents were collected from six adult dogs after humane euthanasia for an unrelated study. The microbiota were characterized using Illumina sequencing of 16S rRNA genes. The metabolome was characterized by mass spectrometry-based methods.

Results

Prevalent phyla throughout the samples were Proteobacteria, Firmicutes, Fusobacteria, and Bacteroidetes, consistent with previous findings in dogs and other species. A total of 530 unique metabolites were detected; 199 of these were identified as previously named compounds, but 141 of them had at least one significantly different site-pair comparison. Noteworthy examples include relative concentrations of amino acids, which decreased from the small to large intestine; pyruvate, which peaked in the ileum; and several phenol-containing carboxylic acid compounds that increased in the large intestine.

Conclusion

The microbiota and metabolome vary significantly at different sites along the canine gastrointestinal tract.

     

Identification of pathogens in culture-negative infective endocarditis cases by metagenomic analysis

Background

Pathogens identification is critical for the proper diagnosis and precise treatment of infective endocarditis (IE). Although blood and valve cultures are the gold standard for IE pathogens detection, many cases are culture-negative, especially in patients who had received long-term antibiotic treatment, and precise diagnosis has therefore become a major challenge in the clinic. Metagenomic sequencing can provide both information on the pathogenic strain and the antibiotic susceptibility profile of patient samples without culturing, offering a powerful method to deal with culture-negative cases.

Methods

To assess the feasibility of a metagenomic approach to detect the causative pathogens in resected valves from IE patients, we employed both next-generation sequencing and Oxford Nanopore Technologies MinION nanopore sequencing for pathogens and antimicrobial resistance detection in seven culture-negative IE patients. Using our in-house developed bioinformatics pipeline, we analyzed the sequencing results generated from both platforms for the direct identification of pathogens from the resected valves of seven clinically culture-negative IE patients according to the modified Duke criteria.

Results

Our results showed both metagenomics methods can be applied for the causative pathogen detection in all IE samples. Moreover, we were able to simultaneously characterize respective antimicrobial resistance features.

Conclusion

Metagenomic methods for IE detection can provide clinicians with valuable information to diagnose and treat IE patients after valve replacement surgery. However, more efforts should be made to optimize protocols for sample processing, sequencing and bioinformatics analysis.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.