Data Analysis Pipeline Research Articles

Measuring single-cell susceptibility to antibiotics within monoclonal bacterial populations.

The emergence of new resistant bacterial strains is a worldwide challenge. A resistant bacterial population can emerge from a single cell that acquires resistance or persistence. Hence, new ways of tackling the mechanism of antibiotic response, such as single cell studies are required. It is necessary to see what happens at the single cell level, in order to understand what happens at the population level. To date, linking the heterogeneity of single-cell susceptibility to the population-scale response to antibiotics remains challenging due to the trade-offs between the resolution and the field of view. Here we present a platform that measures the ability of individual E. coli cells to form small colonies at different ciprofloxacin concentrations, by using anchored microfluidic drops and an image and data analysis pipelines. The microfluidic results are benchmarked against classical microbiology measurements of antibiotic susceptibility, showing an agreement between the pooled microfluidic chip and replated bulk measurements. Further, the experimental likelihood of a single cell to form a colony is used to provide a probabilistic antibiotic susceptibility curve. In addition to the probabilistic viewpoint, the microfluidic format enables the characterization of morphological features over time for a large number of individual cells. This pipeline can be used to compare the response of different bacterial strains to antibiotics with different action mechanisms.

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

Plant Transcriptome Analysis with HISAT-StringTie-Ballgown and TopHat-Cufflinks Pipelines.

The development of next-generation sequencing technology has led to a burst of data in a single assay. Management of a large dataset requires high demands on bioinformatic skills and computing resources. Here we present two popular pipelines for RNA-seq data analysis, using open-source software tools HISAT-StringTie-Ballgown and TopHat-Cufflinks. To meet the need of plant scientist, we describe in detail how to perform such comprehensive analysis beginning with raw RNA-seq reads and available reference genome. It allows biologists to align short reads to a reference genome, measure the transcript abundance, and analyze gene differential expression under two or more conditions. We also discuss other RNA-seq tools that are comparable or alternative to this protocol.

UafR: An R package that automates mass spectrometry data processing.

Chemical information has become increasingly ubiquitous and has outstripped the pace of analysis and interpretation. We have developed an R package, uafR, that automates a grueling retrieval process for gas -chromatography coupled mass spectrometry (GC -MS) data and allows anyone interested in chemical comparisons to quickly perform advanced structural similarity matches. Our streamlined cheminformatics workflows allow anyone with basic experience in R to pull out component areas for tentative compound identifications using the best published understanding of molecules across samples (pubchem.gov). Interpretations can now be done at a fraction of the time, cost, and effort it would typically take using a standard chemical ecology data analysis pipeline. The package was tested in two experimental contexts: (1) A dataset of purified internal standards, which showed our algorithms correctly identified the known compounds with R2 values ranging from 0.827-0.999 along concentrations ranging from 1 × 10-5 to 1 × 103 ng/μl, (2) A large, previously published dataset, where the number and types of compounds identified were comparable (or identical) to those identified with the traditional manual peak annotation process, and NMDS analysis of the compounds produced the same pattern of significance as in the original study. Both the speed and accuracy of GC -MS data processing are drastically improved with uafR because it allows users to fluidly interact with their experiment following tentative library identifications [i.e. after the m/z spectra have been matched against an installed chemical fragmentation database (e.g. NIST)]. Use of uafR will allow larger datasets to be collected and systematically interpreted quickly. Furthermore, the functions of uafR could allow backlogs of previously collected and annotated data to be processed by new personnel or students as they are being trained. This is critical as we enter the era of exposomics, metabolomics, volatilomes, and landscape level, high-throughput chemotyping. This package was developed to advance collective understanding of chemical data and is applicable to any research that benefits from GC -MS analysis. It can be downloaded for free along with sample datasets from Github at github.org/castratton/uafR or installed directly from R or RStudio using the developer tools: 'devtools::install_github("castratton/uafR")'.

Quantifying mitochondrial protein import by mePRODmt proteomics.

Mitochondrial protein import is crucial for maintaining cellular health and homeostasis. Disruptions in this process have been linked to various diseases. Traditional methods for studying mitochondrial protein import predominantly focus on individual proteins and lack the dynamic resolution needed to fully appreciate the complexity of mitochondrial proteostasis and protein trafficking. To address these limitations, we developed a technique called mitochondria-specific multiplexed enhanced protein dynamics (mePRODmt). This method is a novel application of the mePROD methodology and utilizes pulsed stable isotope labeling with amino acids in cell culture (pSILAC)-based proteomics approach to study transient mitochondrial protein import. This chapter outlines the mePRODmt protocol, which includes the preparation of heavy SILAC-labeled peptides for boosting overall mitochondrial peptide signals (booster), SILAC labeling of cultured cells under experimental conditions, mitochondria isolation, sample preparation for multiplex proteomics using tandem mass tags (TMT) for isobaric labeling, recommended liquid chromatography-mass spectrometry (LC-MS) settings for reporter ion quantitation and a data analysis pipeline to analyze pSILAC-TMT data.

The NIKA2 Sunyaev-Zeldovich Large Program

The NIKA2 camera operating at the IRAM 30-m telescope excels in high-angular resolution mapping of the thermal Sunyaev-Zel’dovich effect towards galaxy clusters at intermediate and high-redshift. As part of the NIKA2 guaranteed-time, the SZ Large Program (LPSZ) aims at tSZ-mapping a representative sample of SZ-selected galaxy clusters in the catalogues of the Planck satellite and of the Atacama Cosmology Telescope, and also observed in X-ray with XMM-Newton or Chandra. Having completed observations in January 2023, we present tSZ maps of 38 clusters spanning the targeted mass (3 < M500/1014M⊙ < 10) and redshift (0.5 < z < 0.9) range. The first in-depth studies of individual clusters highlight the potential of combining tSZ and X-ray observations at similar angular resolution for precised mass measurements under the hydrostatic assumption MHSE. These were milestones for the development of a standard data analysis pipeline to go from NIKA2 raw data to the thermodynamic properties of galaxy clusters for the upcoming LPSZ data release. Final products will include measurements of the mean pressure profile of unprecedented quality and MHSE-observable scaling relation using a distinctive SZ-selected sample, which will be key for ultimately improving the accuracy of cluster-based cosmology.

NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data.

Human leukocyte antigen (HLA) typing is of great importance in clinical applications such as organ transplantation, blood transfusion, disease diagnosis and treatment, and forensic analysis. In recent years, nanopore sequencing technology has emerged as a rapid and cost-effective option for HLA typing. However, due to the principles and data characteristics of nanopore sequencing, there was a scarcity of robust and generalizable bioinformatics tools for its downstream analysis, posing a significant challenge in deciphering the thousands of HLA alleles present in the human population. To address this challenge, we developed NanoHLA as a tool for high-resolution typing of HLA class I genes without error correction based on nanopore sequencing. The method integrated the concepts of HLA type coverage analysis and the data conversion techniques employed in Nano2NGS, which was characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. In validation with public nanopore sequencing datasets, NanoHLA showed an overall concordance rate of 84.34% for HLA-A, HLA-B, and HLA-C, and demonstrated superior performance in comparison to existing tools such as HLA-LA. NanoHLA provides tools and solutions for use in HLA typing related fields, and look forward to further expanding the application of nanopore sequencing technology in both research and clinical settings. The code is available at https://github.com/langjidong/NanoHLA .

Summary of ChIP-Seq Methods and Description of an Optimized ChIP-Seq Protocol.

Chromatin immunoprecipitation (ChIP) is an invaluable method to characterize interactions between proteins and genomic DNA, such as the genomic localization of transcription factors and post-translational modification of histones. DNA and proteins are reversibly and covalently crosslinked using formaldehyde. Then the cells are lysed to release the chromatin. The chromatin is fragmented into smaller sizes either by micrococcal nuclease (MN) or sonication and then purified from other cellular components. The protein-DNA complexes are enriched by immunoprecipitation (IP) with antibodies that target the epitope of interest. The DNA is released from the proteins by heat and protease treatment, followed by degradation of contaminating RNAs with RNase. The resulting DNA is analyzed using various methods, including polymerase chain reaction (PCR), quantitative PCR (qPCR), or sequencing. This protocol outlines each of these steps for both yeast and human cells. This chapter includes a contextual discussion of the combination of ChIP with DNA analysis methods such as ChIP-on-Chip, ChIP-qPCR, and ChIP-Seq, recent updates on ChIP-Seq data analysis pipelines, complementary methods for identification of binding sites of DNA binding proteins, and additional protocol information about ChIP-qPCR and ChIP-Seq.

Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers

Large cross-validation campaigns produce classification models from NIR spectroscopy measurements of polyolefins (the most common class of plastic produced), which may improve sortation at recycling facilities.

Analysis and Visualization of Single-Cell Sequencing Data with Scanpy and MetaCell: A Tutorial.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

GBS-DP: a bioinformatics pipeline for processing data coming from genotyping by sequencing

The development of next-generation sequencing technologies has provided new opportunities for genotyping various organisms, including plants. Genotyping by sequencing (GBS) is used to identify genetic variability more rapidly, and is more cost-effective than whole-genome sequencing. GBS has demonstrated its reliability and flexibility for a number of plant species and populations. It has been applied to genetic mapping, molecular marker discovery, genomic selection, genetic diversity studies, variety identification, conservation biology and evolutio nary studies. However, reduction in sequencing time and cost has led to the need to develop efficient bioinformatics analyses for an ever-expanding amount of sequenced data. Bioinformatics pipelines for GBS data analysis serve the purpose. Due to the similarity of data processing steps, existing pipelines are mainly characterised by a combination of software packages specifically selected either to process data for certain organisms or to process data from any organisms. However, despite the usage of efficient software packages, these pipelines have some disadvantages. For example, there is a lack of process automation (in some pipelines, each step must be started manually), which significantly reduces the performance of the analysis. In the majority of pipelines, there is no possibility of automatic installation of all necessary software packages; for most of them, it is also impossible to switch off unnecessary or completed steps. In the present work, we have developed a GBS-DP bioinformatics pipeline for GBS data analysis. The pipeline can be applied for various species. The pipeline is implemented using the Snakemake workflow engine. This implementation allows fully automating the process of calculation and installation of the necessary software packages. Our pipeline is able to perform analysis of large datasets (more than 400 samples).

Pipeline for RNA sequencing data analysis by combination of Nextflow and R

Pipeline for RNA sequencing data analysis by combination of Nextflow and R

The Large Array Survey Telescope—Pipeline. I. Basic Image Reduction and Visit Coaddition

The Large Array Survey Telescope (LAST) is a wide-field telescope designed to explore the variable and transient sky with a high cadence and to be a test-bed for cost-effective telescope design. A LAST node is composed of 48 (32 already deployed), 28 cm f/2.2 telescopes. A single telescope has a 7.4 deg2 field of view and reaches a 5σ limiting magnitude of 19.6 (21.0) in 20 (20 × 20) s (filter-less), while the entire system provides a 355 deg2 field of view. The basic strategy of LAST is to obtain multiple 20 s consecutive exposures of each field (a visit). Each telescope carries a 61 Mpix camera, and the system produces, on average, about 2.2 Gbit s−1. This high data rate is analyzed in near real-time at the observatory site, using limited computing resources (about 700 cores). Given this high data rate, we have developed a new, efficient data reduction and analysis pipeline. The LAST data pipeline includes two major parts: (i) Processing and calibration of single images, followed by a coaddition of the visit’s exposures. (ii) Building the reference images and performing image subtraction and transient detection. Here we describe in detail the first part of the pipeline. Among the products of this pipeline are photometrically and astrometrically calibrated single and coadded images, 32 bit mask images marking a wide variety of problems and states of each pixel, source catalogs built from individual and coadded images, Point-Spread Function photometry, merged source catalogs, proper motion and variability indicators, minor planets detection, calibrated light curves, and matching with external catalogs. The entire pipeline code is made public. Finally, we demonstrate the pipeline performance on real data taken by LAST.

Distinct cultured microglia characteristics distinguish cognitive SuperAgers from their cognitively normal peers

AbstractBackgroundSuperAgers are individuals ≥80 years with exceptional episodic memory equal to or better than individuals 20 to 30 years their junior. SuperAgers display resistance to age‐related neurofibrillary degeneration. Qualitative observations suggested that SuperAger cultured microglia display higher rates of proliferation compared to age‐matched controls. The purpose of this study was a comprehensive and quantitative investigation of potential differences between SuperAger and control microglia.MethodApproximately 6.6×103 microglia from the frontal cortex of 5 SuperAgers and 5 controls were seeded and allowed to proliferate to 80% confluence prior to passage (P). Number of cells at confluence, number of days to confluence, and highest passage reached were recorded. Gene expression profiles were investigated in P4 cultures. RNASeq libraries were prepared using the Lexogen QuantSeq 3’ RNA Library kit and assessed using the Agilent High Sensitivity DNA chip. Bioinformatic analysis was performed using the BlueBee integrated data analysis pipeline. Microglia function was assayed through uptake of fluorescently tagged oligomeric and fibrillar amyloid‐β (Aβ42) and production of reactive oxygen species (ROS) in response to the two Aβ conformations.ResultAt each passage, SuperAger cultures contained greater numbers of microglia when compared with controls (by 150% at P5, p<0.01) and reached 80% confluent faster (by 108%). On average, control microglia ceased proliferating at P9, while SuperAger microglia proliferated up to P12. RNAseq analysis identified 434 genes differentially expressed in the two groups (p<0.05). Upregulated genes in SuperAgers included genes involved in DNA repair and in membrane trafficking. SuperAgers exhibited downregulation of several transcripts involved in age‐related disease pathogenesis, including Adap2, MAP2, RBM20, CNTN3, and genes that regulate mitochondrial oxidative burst. Preliminary functional analysis revealed more efficient uptake and response to oligomeric when compared with fibrillar Aβ. SuperAger microglia displayed greater ROS production when compared with controls. No differences were observed in Aβ uptake.ConclusionThese results point to distinct characteristics of microglia that may contribute to the biological bases of the SuperAging phenotype. Upregulation of genes involved in DNA repair is likely to slow microglia senescence, allowing increased proliferation in SuperAgers. Additional studies are needed to validate and extend these findings.

DNA methylation profiling of individual neurons isolated from post‐mortem brain tissue using laser capture microdissection and adapted limiting dilution bisulfite pyrosequencing

AbstractBackgroundA challenge frequently encountered in neuroepigenomic research, especially in the context of neurodegenerative diseases such as Alzheimer’s disease, is the difficulty of measuring epigenetic patterns in heterogenous bulk tissue samples, as these are known to cause noise during profiling. A viable strategy to overcome this challenge is to analyze epigenetic marks in single neurons that have been isolated using laser capture microdissection (LCM).MethodIn this study, we developed a new approach for targeted DNA methylation profiling of individual genes that combines the use of LCM and limiting dilution bisulfite pyrosequencing (LDBSP). By utilizing this method, we were able to determine the cytosine‐phosphate‐guanine (CpG) methylation rates of single alleles obtained from 50 neurons, which were isolated from unfixed post‐mortem brain tissue.ResultWe demonstrate how the methylation status of multiple genes such as RHBDF2, OXT, TNXB, DNAJB13, PGLYRP1, C3, and LMX1B, can be quantified simultaneously using the method described above. In addition, we provide an adapted data analysis pipeline for LDBSP, which accounts for and corrects methylation rates obtained from multi‐allele reactions. Furthermore, we show that the efficiency of LDBSP on DNA derived from LCM‐isolated neurons is comparable to the efficiency in previous studies using other cell types.ConclusionThis method allows for a more precise determination of the DNA methylation status of each targeted gene in the neuronal cell samples under investigation, thereby increasing the validity of the technique.

Strengthening the Diagnosis of Drug-Resistant Tuberculosis Using NGS-Based Approaches and Bioinformatics Pipelines for Data Analysis in India.

In India, drug-resistant tuberculosis (DR-TB) is a major public health issue and a significant challenge to stop TB program. An estimated 27% of new TB cases and 44% of previously treated TB cases are resistant to at least one anti-TB drug. The conventional methods for DR-TB diagnosis are time-consuming and have limitations, leading to delays in treatment initiation and the spread of the disease. Next-generation sequencing (NGS) based approaches have emerged as a promising tool for diagnosing DR-TB, simultaneously offering rapid and accurate detection of resistance mutations in multiple genes. NGS-based approaches generate a large amount of data, which requires efficient and reliable bioinformatics pipelines for data analysis. TBProfiler and Mykrobe are the bioinformatics pipelines that have been created to analyze NGS data for the diagnosis of DR-TB. These pipelines use reference-based and machine-learning approaches to detect resistance mutations and predict drug susceptibility, enabling clinicians to make informed treatment decisions. Implementing NGS-based approaches and bioinformatics pipelines for DR-TB diagnosis can potentially improve patient outcomes by facilitating early detection of drug resistance and guiding personalized treatment regimens. However, the widespread adoption of these approaches in India faces several challenges, including high costs, limited infrastructure, and a lack of trained personnel. Addressing these challenges requires concerted effort to ensure equitable access to and effective implementation of these innovative technologies.

Unraveling the Evolutionary Paths in Relapsed Early-Stage Follicular Lymphoma Showing Complete Remission

Background: Follicular lymphoma (FL) develops through a stepwise acquisition of cooperative genetic changes with t(14;18)(q32;q21)/ IGH:: BCL2 occurring early at the pre-B stage of B-cell development. Patients with FL typically show an indolent clinical course, remitting and relapsing, and eventually developing resistance to treatments. Interestingly, the majority of transformed FL do not progress directly from the preceding FL but originate from their clonally related lymphoma precursor cells (CLP). In light of this finding, we wanted to address whether such divergent tumor evolution also occurs in the setting of relapses in early-stage FL in patients who achieved complete remission following initial treatment. Methods: We identified 13 cases of FL with an early clinical stage (stage I = 9; stage II = 4) at the initial diagnosis, who showed complete remission (mean: 5 years; range: 1-11.5 years) following local radiotherapy but subsequently relapsed (≥ 2 in 5). Paired formalin-fixed paraffin-embedded diagnostic and relapsed lymphoma biopsies were obtained in each case. BCL2 translocation status was investigated using interphase fluorescence in-situ hybridization (FISH). Genomic DNA was extracted from the tumor-rich areas in each tissue specimen and targeted next-generation sequencing was performed with a customized gene panel comprising 191 genes/ regions that are implicated in FL, diffuse large B-cell lymphoma (DLBCL), and B-cell biology using the TWIST target enrichment kits and Illumina NextSeq platform. Clonality analysis of IG gene rearrangements was performed by combining the BIOMED-2 PCR assays and Illumina MiSeq sequencing. Sequencing data analyses, variant calling, and filtering were performed using a well-established data analysis pipeline from a previous study (Cucco et al., 2018). Results: Interphase FISH showed BCL2 translocation in 6 cases, BCL2 amplification (up to 8 copies) in 1 case, and BCL2 copy number gain (3-4 copies) in 3 cases. Pathogenic variants were identified in each of the lymphoma biopsies and were typical of those frequently seen in FL and DLBCL. There was no significant difference in the mutational burden between the paired diagnostic (mean: 7.8/sample; range: 1-13) and relapsed FL (7.9/sample; range: 2-13), excluding tFL. Both the diagnostic and relapsed lesions showed similar, frequent mutations in BCL2 (33.3% vs 41.67%), CREBBP (58.3% vs 58.3%), EP300 (41.7% vs 41.7%), KMT2D (50% vs 58.3%), TNFRSF14 (66.7% vs 58.3%), and STAT6 (50% vs 50%). The clonal relationship between the diagnostic FL and relapses was confirmed in 11 cases. In 6 cases, common and distinct variants were seen between the paired diagnostic and relapsed lymphomas, indicating their divergent evolution from a CLP. In 2 cases, distinct B-cell clones were involved in the diagnostic and the relapsed lymphomas, including 1 case involving 2 different BCL2 translocations, indicating independent evolution of the new lymphomas. In the remaining 5 cases, the relapsed lymphomas developed via a linear progression from the preceding FL (n = 5). Conclusion: Both divergent evolution, either from clonally related or unrelated precursors, and linear evolution processes are involved in the development of relapses in patients with an early-stage FL following complete remission, with the former being more frequent. The above findings, together with the evidence of divergent evolution of tFL in the majority of cases, challenge the current therapeutic strategy of early-stage FL, which does not target the premalignant cell population that underpins lymphoma recurrence in the majority of cases. Additionally, the routine diagnostic workup, which includes IG gene clonality and BCL2 translocation analyses, cannot address the evolutionary path in clonally related lymphomas. Our results indicate the significance of mutational profiling at diagnosis as well as at relapse, and the need to comprehensively characterize and target the evasive CLP population to prevent recurrence.

Asteroids co-orbital motion classification based on Machine Learning

ABSTRACT In this work, we explore how to classify asteroids in co-orbital motion with a given planet using Machine Learning. We consider four different kinds of motion in mean motion resonance with the planet, nominally Tadpole at L4 and L5, Horseshoe and Quasi-Satellite, building three data sets defined as Real (taking the ephemerides of real asteroids from the JPL Horizons system), Ideal and Perturbed (both simulated, obtained by propagating initial conditions considering two different dynamical systems) for training and testing the Machine Learning algorithms in different conditions. The time series of the variable θ (angle related to the resonance) are studied with a data analysis pipeline defined ad hoc for the problem and composed by: data creation and annotation, time series features extraction thanks to the tsfresh package (potentially followed by selection and standardization) and the application of Machine Learning algorithms for Dimensionality Reduction and Classification. Such approach, based on features extracted from the time series, allows to work with a smaller number of data with respect to Deep Learning algorithms, also allowing to define a ranking of the importance of the features. Physical interpretability of the features is another key point of this approach. In addition, we introduce the SHapley Additive exPlanations for Explainability technique. Different training and test sets are used, in order to understand the power and the limits of our approach. The results show how the algorithms are able to identify and classify correctly the time series, with a high degree of performance.

A multiomic investigation of lung adenocarcinoma molecular subtypes.

Lung adenocarcinoma-an aggressive and life-threatening malignancy-is a type of non-small-cell lung cancer. Despite medical advancements, the prognosis of lung adenocarcinoma remains unfavorable, likely because of its heterogeneous nature. Furthermore, few subtype-specific treatments are available for lung adenocarcinoma. This study was conducted to explore the molecular subtypes of lung adenocarcinoma. We performed a joint analysis of transcriptome and proteome data from East Asian patients with lung adenocarcinoma (nonsmokers, 86.5%). Four novel subtypes were identified based on distinct molecular characteristics: subtypes I, II, III, and IV. In patients with subtype I lung adenocarcinoma, eukaryotic translation initiation factor 4 gamma 1 activates cell proliferation; inhibiting this factor suppresses tumor growth, and reducing its level induces autophagy. Subtype II is characterized by Kristen rat sarcoma viral oncogene homolog-activating oncogenesis; the onset age of this subtype is the lowest among all subtypes. Subtype III manifests as an advanced disease at diagnosis; it is characterized by a core serum response-related oncogenic signature, which indicates poor overall survival in Western patients with lung cancer. Subtype IV is more common in men than in women; it has astroglial characteristics. A Connectivity Map analysis revealed that the oncogenic expression patterns corresponding to subtypes I, II, III, and IV can be reversed by the inhibitors of Inhibitor of κB (IκB) kinase (eg, withaferin A), mammalian target of rapamycin (eg, everolimus), Src proto-oncogene (Src) (eg, saracatinib), and Transforming Growth Factor (TGF)-β/Smad (eg, LY-364947), respectively. This study introduced an innovative multiomics data analysis pipeline. Using this approach, we successfully identified four molecular subtypes of lung adenocarcinoma and their candidate therapeutic agents. The newly identified subtypes can be combined with the current biomarkers to generate a comprehensive roadmap for treatment decision-making.

Performance Analysis of Cross-Assembly of Metatranscriptomic Datasets in Viral Community Studies

We conducted a comparative analysis of individual and cross-assemblies of several metatranscriptomic data sets to study viral communities using several metatranscriptomes of endemic Baikal mollusks. We have shown that, compared to individual dataset assemblies, a Hidden Markov Model-based cross-assembly procedure increases the number of viral contigs (or scaffolds) per sample, the number of virotypes identified, and the average length of scaffolds per sample. The proportion of assembled viral reads from the total number of reads in samples is higher in cross-assembly. De novo cross-genomic assemblies combined with a virus identification algorithm using Hidden Markov Model present the data in a table with the number of reads from different samples for each scaffold. The table allows comparison of samples based on the representation of all viral scaffolds, including those not taxonomically identified, i.e. those that have no analogues in the NCBI RefSeq database. Thus, cross-genomic assemblies allow for comparative analyzes taking into account the latent diversity of viruses. We propose a pipeline for metatranscriptomic data analysis using de novo cross-genomic assembly to study viral diversity.