Comparative transcriptome analysis of Peromyscus leucopus and C3H mice infected with the Lyme disease pathogen.

Lyme disease (LD), the most prevalent tick-borne disease of humans in the Northern Hemisphere, is caused by the spirochetal bacterium of Borreliella burgdorferi (Bb) sensu lato complex. In nature, Bb spirochetes are continuously transmitted between Ixodes ticks and mammalian or avian reservoir hosts. Peromyscus leucopus mice are considered the primary mammalian reservoir of Bb in the United States. Earlier studies demonstrated that experimentally infected P. leucopus mice do not develop disease. In contrast, C3H mice, a widely used laboratory strain of Mus musculus in the LD field, develop severe Lyme arthritis. To date, the exact tolerance mechanism of P. leucopus mice to Bb-induced infection remains unknown. To address this knowledge gap, the present study has compared spleen transcriptomes of P. leucopus and C3H/HeJ mice infected with Bb strain 297 with those of their respective uninfected controls. Overall, the data showed that the spleen transcriptome of Bb-infected P. leucopus mice was much more quiescent compared to that of the infected C3H mice. To date, the current investigation is one of the few that have examined the transcriptome response of natural reservoir hosts to Borreliella infection. Although the experimental design of this study significantly differed from those of two previous investigations, the collective results of the current and published studies have consistently demonstrated very limited transcriptomic responses of different reservoir hosts to the persistent infection of LD pathogens. Importance: The bacterium Borreliella burgdorferi (Bb) causes Lyme disease, which is one of the emerging and highly debilitating human diseases in countries of the Northern Hemisphere. In nature, Bb spirochetes are maintained between hard ticks of Ixodes spp. and mammals or birds. In the United States, the white-footed mouse, Peromyscus leucopus, is one of the main Bb reservoirs. In contrast to humans and laboratory mice (e.g., C3H mice), white-footed mice rarely develop clinical signs (disease) despite being (persistently) infected with Bb. How the white-footed mouse tolerates Bb infection is the question that the present study has attempted to address. Comparisons of genetic responses between Bb-infected and uninfected mice demonstrated that, during a long-term Bb infection, C3H mice reacted much stronger, whereas P. leucopus mice were relatively unresponsive.

In vitro atlas of dorsal spinal interneurons reveals Wnt signaling as a critical regulator of progenitor expansion.

Restoring sensation after injury or disease requires a reproducible method for generating large quantities of bona fide somatosensory interneurons. Toward this goal, we assess the mechanisms by which dorsal spinal interneurons (dIs; dI1-dI6) can be derived from mouse embryonic stem cells (mESCs). Using two developmentally relevant growth factors, retinoic acid (RA) and bone morphogenetic protein (BMP) 4, we recapitulate the complete in vivo program of dI differentiation through a neuromesodermal intermediate. Transcriptional profiling reveals that mESC-derived dIs strikingly resemble endogenous dIs, with the correct molecular and functional signatures. We further demonstrate that RA specifies dI4-dI6 fates through a default multipotential state, while the addition of BMP4 induces dI1-dI3 fates and activates Wnt signaling to enhance progenitor proliferation. Constitutively activating Wnt signaling permits the dramatic expansion of neural progenitor cultures. These cultures retain the capacity to differentiate into diverse populations of dIs, thereby providing a method of increasing neuronal yield.

Pipeline for Analyzing Activity of Metabolic Pathways in Planktonic Communities Using Metatranscriptomic Data.

In this article, we present our novel pipeline for analysis of metabolic activity using a microbial community's metatranscriptome sequence data set for validation. Our method is based on expectation-maximization (EM) algorithm and provides enzyme expression and pathway activity levels. Further expanding our analysis, we consider individual enzymatic activity and compute enzyme participation coefficients to approximate the metabolic pathway activity more accurately. We apply our EM pathways pipeline to a metatranscriptomic data set of a plankton community from surface waters of the Northern Gulf of Mexico. The data set consists of RNA-seq data and respective environmental parameters, which were sampled at two depths, six times a day over multiple 24-hour cycles. Furthermore, we discuss microbial dependence on day-night cycle within our findings based on a three-way correlation of the enzyme expression during antipodal times-midnight and noon. We show that the enzyme participation levels strongly affect the metabolic activity estimates: that is, marginal and multiple linear regression of enzymatic and metabolic pathway activity correlated significantly with the recorded environmental parameters. Our analysis statistically validates that EM-based methods produce meaningful results, as our method confirms statistically significant dependence of metabolic pathway activity on the environmental parameters, such as salinity, temperature, brightness, and a few others.

Single Cell Transcriptomic Analyses Reveal the Impact of bHLH Factors on Human Retinal Organoid Development.

The developing retina expresses multiple bHLH transcription factors. Their precise functions and interactions in uncommitted retinal progenitors remain to be fully elucidated. Here, we investigate the roles of bHLH factors ATOH7 and Neurog2 in human ES cell-derived retinal organoids. Single cell transcriptome analyses identify three states of proliferating retinal progenitors: pre-neurogenic, neurogenic, and cell cycle-exiting progenitors. Each shows different expression profile of bHLH factors. The cell cycle-exiting progenitors feed into a postmitotic heterozygous neuroblast pool that gives rise to early born neuronal lineages. Elevating ATOH7 or Neurog2 expression accelerates the transition from the pre-neurogenic to the neurogenic state, and expands the exiting progenitor and neuroblast populations. In addition, ATOH7 and Neurog2 significantly, yet differentially, enhance retinal ganglion cell and cone photoreceptor production. Moreover, single cell transcriptome analyses reveal that ATOH7 and Neurog2 each assert positive autoregulation, and both suppress key bHLH factors associated with the pre-neurogenic and states and elevate bHLH factors expressed by exiting progenitors and differentiating neuroblasts. This study thus provides novel insight regarding how ATOH7 and Neurog2 impact human retinal progenitor behaviors and neuroblast fate choices.

Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.

Single-cell RNA-sequencing (scRNA-Seq) is a compelling approach to directly and simultaneously measure cellular composition and state, which can otherwise only be estimated by applying deconvolution methods to bulk RNA-Seq estimates. However, it has not yet become a widely used tool in population-scale analyses, due to its prohibitively high cost. Here we show that given the same budget, the statistical power of cell-type-specific expression quantitative trait loci (eQTL) mapping can be increased through low-coverage per-cell sequencing of more samples rather than high-coverage sequencing of fewer samples. We use simulations starting from one of the largest available real single-cell RNA-Seq data from 120 individuals to also show that multiple experimental designs with different numbers of samples, cells per sample and reads per cell could have similar statistical power, and choosing an appropriate design can yield large cost savings especially when multiplexed workflows are considered. Finally, we provide a practical approach on selecting cost-effective designs for maximizing cell-type-specific eQTL power which is available in the form of a web tool.

Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

BATMAN: Fast and Accurate Integration of Single-Cell RNA-Seq Datasets via Minimum-Weight Matching.

Single-cell RNA-sequencing (scRNA-seq) is a set of technologies used to profile gene expression at the level of individual cells. Although the throughput of scRNA-seq experiments is steadily growing in terms of the number of cells, large datasets are not yet commonly generated owing to prohibitively high costs. Integrating multiple datasets into one can improve power in scRNA-seq experiments, and efficient integration is very important for downstream analyses such as identifying cell-type-specific eQTLs. State-of-the-art scRNA-seq integration methods are based on the mutual nearest neighbor paradigm and fail to both correct for batch effects and maintain the local structure of the datasets. In this paper, we propose a novel scRNA-seq dataset integration method called BATMAN (BATch integration via minimum-weight MAtchiNg). Across multiple simulations and real datasets, we show that our method significantly outperforms state-of-the-art tools with respect to existing metrics for batch effects by up to 80% while retaining cell-to-cell relationships.

Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing.

Profiling immunoglobulin (Ig) receptor repertoires with specialized assays can be cost-ineffective and time-consuming. Here we report ImReP, a computational method for rapid and accurate profiling of the Ig repertoire, including the complementary-determining region 3 (CDR3), using regular RNA sequencing data such as those from 8,555 samples across 53 tissues types from 544 individuals in the Genotype-Tissue Expression (GTEx v6) project. Using ImReP and GTEx v6 data, we generate a collection of 3.6 million Ig sequences, termed the atlas of immunoglobulin repertoires (TAIR), across a broad range of tissue types that often do not have reported Ig repertoires information. Moreover, the flow of Ig clonotypes and inter-tissue repertoire similarities across immune-related tissues are also evaluated. In summary, TAIR is one of the largest collections of CDR3 sequences and tissue types, and should serve as an important resource for studying immunological diseases.

Rrp6 Moonlights in an RNA Exosome-Independent Manner to Promote Cell Survival and Gene Expression during Stress.

The nuclear RNA exosome is essential for RNA processing and degradation. Here, we show that the exosome nuclear-specific subunit Rrp6p promotes cell survival during heat stress through the cell wall integrity (CWI) pathway, independently of its catalytic activity or association with the core exosome. Rrp6p exhibits negative genetic interactions with the Slt2/Mpk1p or Paf1p elongation factors required for expression of CWI genes during stress. Overexpression of Rrp6p or of its catalytically inactive or exosome-independent mutants can partially rescue the growth defect of the mpk1Δ mutant and stimulates expression of the Mpk1p target gene FKS2. The rrp6Δ and mpk1Δ mutants show similarities in deficient expression of CWI genes during heat shock, and overexpression of the CWI gene HSP150 can rescue the stress-induced lethality of the mpk1Δrp6Δ mutant. These results demonstrate that Rrp6p moonlights independently from the exosome to ensure proper expression of CWI genes and to promote cell survival during stress.

Benchmarking of computational error-correction methods for next-generation sequencing data.

BACKGROUND: Recent advancements in next-generation sequencing have rapidly improved our ability to study genomic material at an unprecedented scale. Despite substantial improvements in sequencing technologies, errors present in the data still risk confounding downstream analysis and limiting the applicability of sequencing technologies in clinical tools. Computational error correction promises to eliminate sequencing errors, but the relative accuracy of error correction algorithms remains unknown. RESULTS: In this paper, we evaluate the ability of error correction algorithms to fix errors across different types of datasets that contain various levels of heterogeneity. We highlight the advantages and limitations of computational error correction techniques across different domains of biology, including immunogenomics and virology. To demonstrate the efficacy of our technique, we apply the UMI-based high-fidelity sequencing protocol to eliminate sequencing errors from both simulated data and the raw reads. We then perform a realistic evaluation of error-correction methods. CONCLUSIONS: In terms of accuracy, we find that method performance varies substantially across different types of datasets with no single method performing best on all types of examined data. Finally, we also identify the techniques that offer a good balance between precision and sensitivity.

MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples.

BACKGROUND: High throughput sequencing has spurred the development of metagenomics, which involves the direct analysis of microbial communities in various environments such as soil, ocean water, and the human body. Many existing methods based on marker genes or k-mers have limited sensitivity or are too computationally demanding for many users. Additionally, most work in metagenomics has focused on bacteria and archaea, neglecting to study other key microbes such as viruses and eukaryotes. RESULTS: Here we present a method, MiCoP (Microbiome Community Profiling), that uses fast-mapping of reads to build a comprehensive reference database of full genomes from viruses and eukaryotes to achieve maximum read usage and enable the analysis of the virome and eukaryome in each sample. We demonstrate that mapping of metagenomic reads is feasible for the smaller viral and eukaryotic reference databases. We show that our method is accurate on simulated and mock community data and identifies many more viral and fungal species than previously-reported results on real data from the Human Microbiome Project. CONCLUSIONS: MiCoP is a mapping-based method that proves more effective than existing methods at abundance profiling of viruses and eukaryotes in metagenomic samples. MiCoP can be used to detect the full diversity of these communities. The code, data, and documentation are publicly available on GitHub at: https://github.com/smangul1/MiCoP .

Repeat-aware evaluation of scaffolding tools.

Summary: Genomic sequences are assembled into a variable, but large number of contigs that should be scaffolded (ordered and oriented) for facilitating comparative or functional analysis. Finding scaffolding is computationally challenging due to misassemblies, inconsistent coverage across the genome and long repeats. An accurate assessment of scaffolding tools should take into account multiple locations of the same contig on the reference scaffolding rather than matching a repeat to a single best location. This makes mapping of inferred scaffoldings onto the reference a computationally challenging problem. This paper formulates the repeat-aware scaffolding evaluation problem, which is to find a mapping of the inferred scaffolding onto the reference maximizing number of correct links and proposes a scalable algorithm capable of handling large whole-genome datasets. Our novel scaffolding validation framework has been applied to assess the most of state-of-the-art scaffolding tools on the representative subset of Genome Assembly Golden-Standard Evaluations (GAGE) datasets and some novel simulated datasets. Availability and implementation: The source code of this evaluation framework is available at https://github.com/mandricigor/repeat-aware. The documentation is hosted at https://mandricigor.github.io/repeat-aware. Supplementary information: Supplementary data are available at Bioinformatics online.

QUENTIN: reconstruction of disease transmissions from viral quasispecies genomic data.

Motivation: Genomic analysis has become one of the major tools for disease outbreak investigations. However, existing computational frameworks for inference of transmission history from viral genomic data often do not consider intra-host diversity of pathogens and heavily rely on additional epidemiological data, such as sampling times and exposure intervals. This impedes genomic analysis of outbreaks of highly mutable viruses associated with chronic infections, such as human immunodeficiency virus and hepatitis C virus, whose transmissions are often carried out through minor intra-host variants, while the additional epidemiological information often is either unavailable or has a limited use. Results: The proposed framework QUasispecies Evolution, Network-based Transmission INference (QUENTIN) addresses the above challenges by evolutionary analysis of intra-host viral populations sampled by deep sequencing and Bayesian inference using general properties of social networks relevant to infection dissemination. This method allows inference of transmission direction even without the supporting case-specific epidemiological information, identify transmission clusters and reconstruct transmission history. QUENTIN was validated on experimental and simulated data, and applied to investigate HCV transmission within a community of hosts with high-risk behavior. It is available at https://github.com/skumsp/QUENTIN. Contact: pskums@gsu.edu or alexz@cs.gsu.edu or rahul@sfsu.edu or yek0@cdc.gov. Supplementary information: Supplementary data are available at Bioinformatics online.

Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data.

SUMMARY: This note presents IsoEM2 and IsoDE2, new versions with enhanced features and faster runtime of the IsoEM and IsoDE packages for expression level estimation and differential expression. IsoEM2 estimates fragments per kilobase million (FPKM) and transcript per million (TPM) levels for genes and isoforms with confidence intervals through bootstrapping, while IsoDE2 performs differential expression analysis using the bootstrap samples generated by IsoEM2. Both tools are available with a command line interface as well as a graphical user interface (GUI) through wrappers for the Galaxy platform. AVAILABILITY AND IMPLEMENTATION: The source code of this software suite is available at https://github.com/mandricigor/isoem2. The Galaxy wrappers are available at https://toolshed.g2.bx.psu.edu/view/saharlcc/isoem2_isode2/. CONTACT: imandric1@student.gsu.edu or ion@engr.uconn.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Inferring metabolic pathway activity levels from RNA-Seq data.

BACKGROUND: Assessing pathway activity levels is a plausible way to quantify metabolic differences between various conditions. This is usually inferred from microarray expression data. Wide availability of NGS technology has triggered a demand for bioinformatics tools capable of analyzing pathway activity directly from RNA-Seq data. In this paper we introduce XPathway, a set of tools that compares pathway activity analyzing mapping of contigs assembled from RNA-Seq reads to KEGG pathways. The XPathway analysis of pathway activity is based on expectation maximization and topological properties of pathway graphs. RESULTS: XPathway tools have been applied to RNA-Seq data from the marine bryozoan Bugula neritina with and without its symbiotic bacterium "Candidatus Endobugula sertula". We successfully identified several metabolic pathways with differential activity levels. The expression of enzymes from the identified pathways has been further validated through quantitative PCR (qPCR). CONCLUSIONS: Our results show that XPathway is able to detect and quantify the metabolic difference in two samples. The software is implemented in C, Python and shell scripting and is capable of running on Linux/Unix platforms. The source code and installation instructions are available at http://alan.cs.gsu.edu/NGS/?q=content/xpathway .

ScaffMatch: scaffolding algorithm based on maximum weight matching.

MOTIVATION: Next-generation high-throughput sequencing has become a state-of-the-art technique in genome assembly. Scaffolding is one of the main stages of the assembly pipeline. During this stage, contigs assembled from the paired-end reads are merged into bigger chains called scaffolds. Because of a high level of statistical noise, chimeric reads, and genome repeats the problem of scaffolding is a challenging task. Current scaffolding software packages widely vary in their quality and are highly dependent on the read data quality and genome complexity. There are no clear winners and multiple opportunities for further improvements of the tools still exist. RESULTS: This article presents an efficient scaffolding algorithm ScaffMatch that is able to handle reads with both short (<600 bp) and long (>35 000 bp) insert sizes producing high-quality scaffolds. We evaluate our scaffolding tool with the F score and other metrics (N50, corrected N50) on eight datasets comparing it with the most available packages. Our experiments show that ScaffMatch is the tool of preference for the most datasets. AVAILABILITY AND IMPLEMENTATION: The source code is available at http://alan.cs.gsu.edu/NGS/?q=content/scaffmatch. CONTACT: mandric@cs.gsu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.