<Emphasis Type="Italic">NIPTeR</Emphasis>: an R package for fast and accurate trisomy prediction in non-invasive prenatal testing

Background

Various algorithms have been developed to predict fetal trisomies using cell-free DNA in non-invasive prenatal testing (NIPT). As basis for prediction, a control group of non-trisomy samples is needed. Prediction accuracy is dependent on the characteristics of this group and can be improved by reducing variability between samples and by ensuring the control group is representative for the sample analyzed.

Results

NIPTeR is an open-source R Package that enables fast NIPT analysis and simple but flexible workflow creation, including variation reduction, trisomy prediction algorithms and quality control. This broad range of functions allows users to account for variability in NIPT data, calculate control group statistics and predict the presence of trisomies.

Conclusion

NIPTeR supports laboratories processing next-generation sequencing data for NIPT in assessing data quality and determining whether a fetal trisomy is present. NIPTeR is available under the GNU LGPL v3 license and can be freely downloaded from https://github.com/molgenis/NIPTeR or CRAN.

     

Modular and configurable optimal sequence alignment software: <Emphasis Type="Italic">Cola</Emphasis>

Background

The fundamental challenge in optimally aligning homologous sequences is to define a scoring scheme that best reflects the underlying biological processes. Maximising the overall number of matches in the alignment does not always reflect the patterns by which nucleotides mutate. Efficiently implemented algorithms that can be parameterised to accommodate more complex non-linear scoring schemes are thus desirable.

Results

We present Cola, alignment software that implements different optimal alignment algorithms, also allowing for scoring contiguous matches of nucleotides in a nonlinear manner. The latter places more emphasis on short, highly conserved motifs, and less on the surrounding nucleotides, which can be more diverged. To illustrate the differences, we report results from aligning 14,100 sequences from 3' untranslated regions of human genes to 25 of their mammalian counterparts, where we found that a nonlinear scoring scheme is more consistent than a linear scheme in detecting short, conserved motifs.

Conclusions

Cola is freely available under LPGL from https://github.com/nedaz/cola.

     

Complexity and algorithms for copy-number evolution problems

Background

Cancer is an evolutionary process characterized by the accumulation of somatic mutations in a population of cells that form a tumor. One frequent type of mutations is copy number aberrations, which alter the number of copies of genomic regions. The number of copies of each position along a chromosome constitutes the chromosome’s copy-number profile. Understanding how such profiles evolve in cancer can assist in both diagnosis and prognosis.

Results

We model the evolution of a tumor by segmental deletions and amplifications, and gauge distance from profile \(\mathbf {a}\) to \(\mathbf {b}\) by the minimum number of events needed to transform \(\mathbf {a}\) into \(\mathbf {b}\). Given two profiles, our first problem aims to find a parental profile that minimizes the sum of distances to its children. Given k profiles, the second, more general problem, seeks a phylogenetic tree, whose k leaves are labeled by the k given profiles and whose internal vertices are labeled by ancestral profiles such that the sum of edge distances is minimum.

Conclusions

For the former problem we give a pseudo-polynomial dynamic programming algorithm that is linear in the profile length, and an integer linear program formulation. For the latter problem we show it is NP-hard and give an integer linear program formulation that scales to practical problem instance sizes. We assess the efficiency and quality of our algorithms on simulated instances.

Availability

https://github.com/raphael-group/CNT-ILP

     

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments

Background

Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool.

Methods

We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems.

Results

We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity.

Conclusions

Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex).

     

integRATE: a desirability-based data integration framework for the prioritization of candidate genes across heterogeneous omics and its application to preterm birth

Background

The integration of high-quality, genome-wide analyses offers a robust approach to elucidating genetic factors involved in complex human diseases. Even though several methods exist to integrate heterogeneous omics data, most biologists still manually select candidate genes by examining the intersection of lists of candidates stemming from analyses of different types of omics data that have been generated by imposing hard (strict) thresholds on quantitative variables, such as P-values and fold changes, increasing the chance of missing potentially important candidates.

Methods

To better facilitate the unbiased integration of heterogeneous omics data collected from diverse platforms and samples, we propose a desirability function framework for identifying candidate genes with strong evidence across data types as targets for follow-up functional analysis. Our approach is targeted towards disease systems with sparse, heterogeneous omics data, so we tested it on one such pathology: spontaneous preterm birth (sPTB).

Results

We developed the software integRATE, which uses desirability functions to rank genes both within and across studies, identifying well-supported candidate genes according to the cumulative weight of biological evidence rather than based on imposition of hard thresholds of key variables. Integrating 10 sPTB omics studies identified both genes in pathways previously suspected to be involved in sPTB as well as novel genes never before linked to this syndrome. integRATE is available as an R package on GitHub (https://github.com/haleyeidem/integRATE).

Conclusions

Desirability-based data integration is a solution most applicable in biological research areas where omics data is especially heterogeneous and sparse, allowing for the prioritization of candidate genes that can be used to inform more targeted downstream functional analyses.

     

Metabolic response of porcine colon explants to in vitro infection by <Emphasis Type="Italic">Brachyspira hyodysenteriae</Emphasis>: a leap into disease pathophysiology

Introduction

Swine dysentery caused by Brachyspira hyodysenteriae is a production limiting disease in pig farming. Currently antimicrobial therapy is the only treatment and control method available.

Objective

The aim of this study was to characterize the metabolic response of porcine colon explants to infection by B. hyodysenteriae.

Methods

Porcine colon explants exposed to B. hyodysenteriae were analyzed for histopathological, metabolic and pro-inflammatory gene expression changes.

Results

Significant epithelial necrosis, increased levels of l-citrulline and IL-1α were observed on explants infected with B. hyodysenteriae.

Conclusions

The spirochete induces necrosis in vitro likely through an inflammatory process mediated by IL-1α and NO.

     

<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.

     

Iridoid and phenylethanoid/phenylpropanoid metabolite profiles of <Emphasis Type="Italic">Scrophularia</Emphasis> and <Emphasis Type="Italic">Verbascum</Emphasis> species used medicinally in North America

Introduction

Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.

Objectives

We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.

Methods

Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.

Results

Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.

Conclusions

Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.

     

NormalizeMets: assessing,selecting and implementing statistical methods for normalizing metabolomics data

Introduction

In metabolomics studies, unwanted variation inevitably arises from various sources. Normalization, that is the removal of unwanted variation, is an essential step in the statistical analysis of metabolomics data. However, metabolomics normalization is often considered an imprecise science due to the diverse sources of variation and the availability of a number of alternative strategies that may be implemented.

Objectives

We highlight the need for comparative evaluation of different normalization methods and present software strategies to help ease this task for both data-oriented and biological researchers.

Methods

We present NormalizeMets—a joint graphical user interface within the familiar Microsoft Excel and freely-available R software for comparative evaluation of different normalization methods. The NormalizeMets R package along with the vignette describing the workflow can be downloaded from https://cran.r-project.org/web/packages/NormalizeMets/. The Excel Interface and the Excel user guide are available on https://metabolomicstats.github.io/ExNormalizeMets.

Results

NormalizeMets allows for comparative evaluation of normalization methods using criteria that depend on the given dataset and the ultimate research question. Hence it guides researchers to assess, select and implement a suitable normalization method using either the familiar Microsoft Excel and/or freely-available R software. In addition, the package can be used for visualisation of metabolomics data using interactive graphical displays and to obtain end statistical results for clustering, classification, biomarker identification adjusting for confounding variables, and correlation analysis.

Conclusion

NormalizeMets is designed for comparative evaluation of normalization methods, and can also be used to obtain end statistical results. The use of freely-available R software offers an attractive proposition for programming-oriented researchers, and the Excel interface offers a familiar alternative to most biological researchers. The package handles the data locally in the user’s own computer allowing for reproducible code to be stored locally.

     

A composite genome approach to identify phylogenetically informative data from next-generation sequencing

Background

Improvements in sequencing technology now allow easy acquisition of large datasets; however, analyzing these data for phylogenetics can be challenging. We have developed a novel method to rapidly obtain homologous genomic data for phylogenetics directly from next-generation sequencing reads without the use of a reference genome. This software, called SISRS, avoids the time consuming steps of de novo whole genome assembly, multiple genome alignment, and annotation.

Results

For simulations SISRS is able to identify large numbers of loci containing variable sites with phylogenetic signal. For genomic data from apes, SISRS identified thousands of variable sites, from which we produced an accurate phylogeny. Finally, we used SISRS to identify phylogenetic markers that we used to estimate the phylogeny of placental mammals. We recovered eight phylogenies that resolved the basal relationships among mammals using datasets with different levels of missing data. The three alternate resolutions of the basal relationships are consistent with the major hypotheses for the relationships among mammals, all of which have been supported previously by different molecular datasets.

Conclusions

SISRS has the potential to transform phylogenetic research. This method eliminates the need for expensive marker development in many studies by using whole genome shotgun sequence data directly. SISRS is open source and freely available at https://github.com/rachelss/SISRS/releases.

     

PanDelos: a dictionary-based method for pan-genome content discovery

Background

Pan-genome approaches afford the discovery of homology relations in a set of genomes, by determining how some gene families are distributed among a given set of genomes. The retrieval of a complete gene distribution among a class of genomes is an NP-hard problem because computational costs increase with the number of analyzed genomes, in fact, all-against-all gene comparisons are required to completely solve the problem. In presence of phylogenetically distant genomes, due to the variability introduced in gene duplication and transmission, the task of recognizing homologous genes becomes even more difficult. A challenge on this field is that of designing fast and adaptive similarity measures in order to find a suitable pan-genome structure of homology relations.

Results

We present PanDelos, a stand alone tool for the discovery of pan-genome contents among phylogenetic distant genomes. The methodology is based on information theory and network analysis. It is parameter-free because thresholds are automatically deduced from the context. PanDelos avoids sequence alignment by introducing a measure based on k-mer multiplicity. The k-mer length is defined according to general arguments rather than empirical considerations. Homology candidate relations are integrated into a global network and groups of homologous genes are extracted by applying a community detection algorithm.

Conclusions

PanDelos outperforms existing approaches, Roary and EDGAR, in terms of running times and quality content discovery. Tests were run on collections of real genomes, previously used in analogous studies, and in synthetic benchmarks that represent fully trusted golden truth. The software is available at https://github.com/GiugnoLab/PanDelos.

     

Genotypic and metabolic approaches towards the segregation of <Emphasis Type="Italic">Klebsiella pneumoniae</Emphasis> strains producing different antibiotic resistant enzymes

Introduction

Genotype and metabolomic variation are important for bacterial survival and adaptation to environmental changes.

Objectives

In this study, we compared the relationship among Klebsiella pneumoniae strains based on their genotypic and metabolic profiles. In addition, we also evaluated the association of the relationship with beta-lactamase production.

Methods

A total of 53 K. pneumoniae strains isolated in 2013–2014 from a tertiary teaching hospital in Malaysia were subjected to antimicrobial susceptibility testing (AST) via disk diffusion method and beta-lactamase production confirmation. The bacterial strains were also typed genotypically and metabolically via REP-PCR and ¹H-NMR spectroscopy respectively. The concordance of the matrices derived based on genotypic and metabolic characterization was measured based on Spearman’s rank correlation.

Results

Spearman’s correlation rank showed that there is a weak but significant negative correlation between the genetic fingerprints and metabolic profiles of K. pneumoniae. Specifically, K. pneumoniae strains were clustered into five major clusters based on REP-PCR where most of the carbapenem resistant K. pneumoniae (CRKP) strains made up the major cluster. In contrast, metabolic patterns of the three groups (i.e. CRKP, extended spectrum beta-lactamase producing K. pneumoniae (ESBL), susceptible) of K. pneumoniae were clearly differentiated on PLS-DA score plots derived from ¹H-NMR spectroscopy.

Conclusion

Overall, this study showed that metabolomic profiling using ¹H-NMR spectroscopy is able to discriminate K. pneumoniae strains based on their beta-lactamase production status.

     

GATC: a genetic algorithm for gene tree construction under the Duplication-Transfer-Loss model of evolution

Background

Several methods have been developed for the accurate reconstruction of gene trees. Some of them use reconciliation with a species tree to correct, a posteriori, errors in gene trees inferred from multiple sequence alignments. Unfortunately the best fit to sequence information can be lost during this process.

Results

We describe GATC, a new algorithm for reconstructing a binary gene tree with branch length. GATC returns optimal solutions according to a measure combining both tree likelihood (according to sequence evolution) and a reconciliation score under the Duplication-Transfer-Loss (DTL) model. It can either be used to construct a gene tree from scratch or to correct trees infered by existing reconstruction method, making it highly flexible to various input data types. The method is based on a genetic algorithm acting on a population of trees at each step. It substantially increases the efficiency of the phylogeny space exploration, reducing the risk of falling into local minima, at a reasonable computational time. We have applied GATC to a dataset of simulated cyanobacterial phylogenies, as well as to an empirical dataset of three reference gene families, and showed that it is able to improve gene tree reconstructions compared with current state-of-the-art algorithms.

Conclusion

The proposed algorithm is able to accurately reconstruct gene trees and is highly suitable for the construction of reference trees. Our results also highlight the efficiency of multi-objective optimization algorithms for the gene tree reconstruction problem. GATC is available on Github at: https://github.com/UdeM-LBIT/GATC.

     

Study on the Clinical Features and Prognosis of <Emphasis Type="Italic">Penicilliosis marneffei</Emphasis> Without Human Immunodeficiency Virus Infection

Objective

To improve the diagnosis and treatment of Penicilliosis marneffei without human immunodeficiency virus infection.

Methods

Analyze and review the clinical features, diagnosis and treatment of six cases of P. marneffei without human immunodeficiency virus infection at The First Affiliated Hospital of Fujian Medical University.

Results

Two cases were diagnosed in the ENT Department, three cases in the respiratory department and one case in the dermatological department. Penicillium marneffei infection was confirmed by sputum culture, blood culture and tissue biopsy. After definite diagnosis, one refused further treatment, and others showed significant improvement.

Conclusion

Penicilliosis marneffei is insidious onset and easy to be escaped and misdiagnosed. To achieve early diagnosis and appropriate treatment, doubtful cases should be alerted for the diagnoses as P. marneffei.

     

Clinical analysis of germline copy number variation in DMD using a non-conjugate hierarchical Bayesian model

Background

Detection of copy number variants (CNVs) is an important aspect of clinical testing for several disorders, including Duchenne muscular dystrophy, and is often performed using multiplex ligation-dependent probe amplification (MLPA). However, since many genetic carrier screens depend instead on next-generation sequencing (NGS) for wider discovery of small variants, they often do not include CNV analysis. Moreover, most computational techniques developed to detect CNVs from exome sequencing data are not suitable for carrier screening, as they require matched normals, very large cohorts, or extensive gene panels.

Methods

We present a computational software package, geneCNV (http://github.com/vkozareva/geneCNV), which can identify exon-level CNVs using exome sequencing data from only a few genes. The tool relies on a hierarchical parametric model trained on a small cohort of reference samples.

Results

Using geneCNV, we accurately inferred heterozygous CNVs in the DMD gene across a cohort of 15 test subjects. These results were validated against MLPA, the current standard for clinical CNV analysis in DMD. We also benchmarked the tool’s performance against other computational techniques and found comparable or improved CNV detection in DMD using data from panels ranging from 4,000 genes to as few as 8 genes.

Conclusions

geneCNV allows for the creation of cost-effective screening panels by allowing NGS sequencing approaches to generate results equivalent to bespoke genotyping assays like MLPA. By using a parametric model to detect CNVs, it also fulfills regulatory requirements to define a reference range for a genetic test. It is freely available and can be incorporated into any Illumina sequencing pipeline to create clinical assays for detection of exon duplications and deletions.

     

A versatile <Emphasis Type="Italic">Trichoderma reesei</Emphasis> expression system for the production of heterologous proteins

Objectives

To develop a versatile Trichoderma reesei (teleomorph Hypocrea jecorina) expression system for the high-purity production of heterologous proteins.

Results

The versatile T. reesei expression system is based on xyn1 and xyn2 promoters, A824V transition in XYRI, and a bicomponent carbon source strategy. Red fluorescent protein gene rfp and alkaline endoglucanase EGV gene egv3 from Humicola insolens were used as reporter genes to test our versatile expression system

Conclusions

The versatile T. reesei expression system can be applied to produce heterologous proteins with high purity and high yield.

     

Plant growth-promoting bacterium <Emphasis Type="Italic">Pseudomonas fluorescens</Emphasis> FR1 secrets a novel type of extracellular polyhydroxybutyrate polymerase involved in abiotic stress response in plants

Objectives

Identification of novel microbial factors contributing to plant protection against abiotic stress.

Results

The genome of plant growth-promoting bacterium Pseudomonas fluorescens FR1 contains a short mobile element encoding a novel type of extracellular polyhydroxybutyrate (PHB) polymerase (PhbC) associated with a type I secretion system. Genetic analysis using a phbC mutant strain and plants showed that this novel extracellular enzyme is related to the PHB production in planta and suggests that PHB could be a beneficial microbial compound synthesized during plant adaptation to cold stress.

Conclusion

Extracellular PhbC can be used as a new tool for improve crop production under abiotic stress.