High-throughput Sequencing Datasets Research Articles

Abstract 2427: Identification of transcription factors activated in glioblastoma multiforme

Abstract Introduction Glioblastoma multiforme (GBM) is the most common intrinsic brain tumor in adults and is universally fatal. Our strategy to study the molecular mechanisms leading to GBM is to investigate the role of transcription factors (TFs) that are differentially expressed between normal and GBM. Previous studies have shown that ZFX is a key TF that shows increased expression in GBM. However, the role of ZFX, and the identity of TFs that may work with ZFX, in driving GBM carcinogenesis is not yet understood. Materials and methods: ChIP-seq was performed to determine ZFX binding sites in the U87 GBM cancer cell line. RNA-seq datasets generated from GBM tissues by The Cancer Genome Atlas (TCGA) were analyzed to identify TFs showing increased expression in GBM. Publicly available H3K27ac chromatin immunoprecipitation (ChIP-seq) and Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) datasets generated from GBM tissues were analyzed to identify active enhancers and TFs that bind to GBM enhancers. Results: We found that a majority of ZFX binding sites in GBM are located at promoters, with ~1000 binding sites being at non-promoter regions. An overlap analysis of ZFX binding sites in multiple cancer cell types showed that a high percentage of ZFX-binding sites at promoter regions are shared across cell types whereas hundreds of GBM-specific binding sites are located at non-promoter regions. Using TCGA GBM RNA-seq datasets, we next identified differentially expressed TFs. For example, we found that MTY1L and NEUROD2, which have been reported to be tumor suppressors, are downregulated while HOX family TFs are upregulated in GBM tissues, as compared to normal tissues. We also identified thousands of enhancers specifically active in GBM, as compared to normal cells, using H3K27ac ChIP-seq and ATAC-seq datasets. TF motif analysis of the identified enhancers revealed that motifs of Basic Leucine Zipper Domain (bZIP), CAAT box-binding transcription factor (CTF), homeobox (HOX), signal transducer and activator of transcription (STAT), and helix-turn-helix (HTH) family were enriched. Conclusion: By integrating transcriptomic and epigenomic datasets, we identified and characterized TFs activated in GBM. We are currently investigating how ZFX cooperates with the TFs we identified as bound to the GBM-specific enhancers. These studies will provide insight into the molecular mechanisms underlying GBM. Citation Format: Zexun Wu, Shannon Schreiner, Peggy J. Farnham, Suhn K. Rhie. Identification of transcription factors activated in glioblastoma multiforme [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2427.

Identification of a High-Frequency Intrahost SARS-CoV-2 Spike Variant with Enhanced Cytopathic and Fusogenic Effects.

ABSTRACTThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that is continuously evolving. Although its RNA-dependent RNA polymerase exhibits some exonuclease proofreading activity, viral sequence diversity can be produced by replication errors and host factors. A diversity of genetic variants can be observed in the intrahost viral population structure of infected individuals. Most mutations will follow a neutral molecular evolution and will not make significant contributions to variations within and between infected hosts. Herein, we profiled the intrasample genetic diversity of SARS-CoV-2 variants, also known as quasispecies, using high-throughput sequencing data sets from 15,289 infected individuals and infected cell lines. Despite high mutational background, we identified recurrent intragenetic variable positions in the samples analyzed, including several positions at the end of the gene encoding the viral spike (S) protein. Strikingly, we observed a high frequency of C→A missense mutations resulting in the S protein lacking the last 20 amino acids (SΔ20). We found that this truncated S protein undergoes increased processing and increased syncytium formation, presumably due to escaping M protein retention in intracellular compartments. Our findings suggest the emergence of a high-frequency viral sublineage that is not horizontally transmitted but potentially involved in intrahost disease cytopathic effects.

Meta-Ribosomalomics: RNA Sequencing Is an Unbiased Method for Parasite Detection of Different Sample Types.

In this perspective article, we review the past use of ribosomal sequences to address scientific and diagnostic questions. We highlight a variety of sequencing approaches including metagenomics and DNA barcoding and their different demands and requirements. Meta-ribosomalomics is introduced as an unbiased approach to exploit high-throughput sequencing datasets for eukaryotic and prokaryotic ribosomal sequences. Prerequisites, benefits, drawbacks, and future perspectives are elaborated and compared to other sequencing approaches.

Variable Populations of Grapevine Virus T Are Present in Vineyards of Hungary.

Grapevine virus T (GVT) is a recently described foveavirus, which was identified from a transcriptome of a Teroldego grapevine cultivar in 2017. Recently, we surveyed vineyards and rootstock plantations in Hungary using small RNA (sRNA) high-throughput sequencing (HTS), at a time when GVT had not yet been described. A re-analysis of our sRNA HTS datasets and a survey of grapevines by RT-PCR revealed the presence of GVT in most of the vineyards tested, while at rootstock fields its presence was very rare. The presence and high variability of the virus in the country was confirmed by sequence analysis of strains originating from different vineyards. In this study, we demonstrate the presence of GVT in Hungary and show its high diversity, suggesting that GVT presence may not seriously affect grapevine health and that it could have been present in European vineyards for a long time as a latent infection.

M6ADD: a comprehensive database of m6A modifications in diseases

ABSTRACT N6-methyladenosine (m6A) modification is an important regulatory factor affecting diseases, including multiple cancers and it is a developing direction for targeted disease therapy. Here, we present the M6ADD (m6A-diseases database) database, a public data resource containing manually curated data on potential m6A-disease associations for which some experimental evidence is available; the related high-throughput sequencing data are also provided and analysed by using different computational methods. To give researchers a tool to query the m6A modification data, the M6ADD was designed as a web-based comprehensive resource focusing on the collection, storage and online analysis of m6A modifications, aimed at exploring the associations between m6A modification and gene disorders and diseases. The M6ADD includes 222 experimentally confirmed m6A-disease associations, involving 59 diseases from a review of more than 2000 published papers. The M6ADD also includes 409,229 m6A-disease associations obtained by computational and statistical methods from 30 high-throughput sequencing datasets. In addition, we provide data on 5239 potential m6A regulatory proteins related to 24 cancers based on network analysis prediction methods. In addition, we have developed a tool to explore the function of m6A-modified genes through the protein–protein interaction networks. The M6ADD can be accessed at http://m6add.edbc.org/.

High-throughput sequencing of the kelp Alaria (Phaeophyceae) reveals epi-endobiotic associations, including a likely phaeophycean parasite

ABSTRACT Whole genome sequencing datasets present the opportunity to not only study evolution in the target organism, but also the associated holobiont. The capacity to study epi-endobiotic kelp associations is improving substantially with the increased availability of high-throughput sequencing datasets. The goal of this study was to determine if shotgun sequencing libraries could be used to document epi- and endophyte/faunal species colonizing Alaria kelp sporophytes from Kamchatka (Russia), the Bay of Fundy (Atlantic Canada) and Nuuk (Greenland). Mitochondrial coxI and plastid rbcL reads were extracted and assembled from six Alaria whole genome sequencing datasets. In total, contigs representing 11 epi-endobiotic species were assembled, of which Chordariacean diversity dominated. Given the presence of a newly discovered phaeophycean coxI sequence lacking an rbcL counterpart, we secondarily tested our hypothesis that the coxI sequence belonged to a phaeophycean parasite. The entire read dataset was assembled for the Alaria specimen hosting the putative parasite, the mitochondrial genome was retrieved, and plastid scaffolds were annotated and screened for phylogenetic placement matching the coxI sequence. The mitochondrial genome of the candidate parasite displayed numerous atypical features, including duplicated genes and rearrangements, and clear signs of relaxed selection, in line with the notion this organism may have a deviant lifestyle. The plastid genome was recovered as several fragments and lacked genes for photosystem and cytochrome complexes and chlorophyll biosynthesis, confirming our hypothesis that the unknown phaeophycean represented a parasitic species. Furthermore, classification to order remained unclear for the phaeophycean parasite, suggesting this species could represent a newly discovered higher-level lineage. Our study showcases the utility of whole-genome sequencing datasets in revealing surprising aspects of the eukaryotic diversity inhabiting kelp holobionts.

Identification of hub genes related to the progression of type 1 diabetes by computational analysis

BackgroundType 1 diabetes (T1D) is a serious threat to childhood life and has fairly complicated pathogenesis. Profound attempts have been made to enlighten the pathogenesis, but the molecular mechanisms of T1D are still not well known.MethodsTo identify the candidate genes in the progression of T1D, expression profiling by high throughput sequencing dataset GSE123658 was downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and gene ontology (GO) and pathway enrichment analyses were performed. The protein-protein interaction network (PPI), modules, target gene - miRNA regulatory network and target gene - TF regulatory network analysis were constructed and analyzed using HIPPIE, miRNet, NetworkAnalyst and Cytoscape. Finally, validation of hub genes was conducted by using ROC (Receiver operating characteristic) curve and RT-PCR analysis. A molecular docking study was performed.ResultsA total of 284 DEGs were identified, consisting of 142 up regulated genes and 142 down regulated genes. The gene ontology (GO) and pathways of the DEGs include cell-cell signaling, vesicle fusion, plasma membrane, signaling receptor activity, lipid binding, signaling by GPCR and innate immune system. Four hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell-cell signaling, cytokine signaling in immune system, signaling by GPCR and innate immune system. ROC curve and RT-PCR analysis showed that EGFR, GRIN2B, GJA1, CAP2, MIF, POLR2A, PRKACA, GABARAP, TLN1 and PXN might be involved in the advancement of T1D. Molecular docking studies showed high docking score.ConclusionsDEGs and hub genes identified in the present investigation help us understand the molecular mechanisms underlying the advancement of T1D, and provide candidate targets for diagnosis and treatment of T1D.

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in these large datasets, in particular the identification of therapeutic antibodies (Abs) or those associated with disease exposure and protection. Here, we describe our efforts to use artificial intelligence (AI)-based image-analyses for prospective classification of Abs based solely on sequence information. We hypothesized that Abs recognizing the same part of an antigen share a limited set of features at the binding interface, and that the binding site regions of these Abs share share common structure and physicochemical property patterns that can serve as a "fingerprint" to recognize uncharacterized Abs. We combined large-scale sequence-based protein-structure predictions to generate ensembles of 3-D Ab models, reduced the Ab binding interface to a 2-D image (fingerprint), used pre-trained convolutional neural networks to extract features, and trained deep neural networks (DNNs) to classify Abs. We evaluated this approach using Ab sequences derived from human HIV and Ebola viral infections to differentiate between two Abs, Abs belonging to specific B-cell family lineages, and Abs with different epitope preferences. In addition, we explored a different type of DNN method to detect one class of Abs from a larger pool of Abs. Testing on Ab sets that had been kept aside during model training, we achieved average prediction accuracies ranging from 71-96% depending on the complexity of the classification task. The high level of accuracies reached during these classification tests suggests that the DNN models were able to learn a series of structural patterns shared by Abs belonging to the same class. The developed methodology provides a means to apply AI-based image recognition techniques to analyze high-throughput B-cell sequencing datasets (repertoires) for Ab classification.

Using Elevated Cholesterol Synthesis as a Prognostic Marker in Wilms’ Tumor: A Bioinformatic Analysis

Background Wilms tumor is the most common renal malignancy of children. Identifying factors that could predict the prognosis of patients with Wilms tumor is clinically meaningful. Many studies found tumors with elevated cholesterol synthesis that are featured with dismal prognosis. Even in some clinical trials, people with excessive dietary cholesterol intake and high plasma low-density lipoprotein levels are observed to have increased risk for cancer. However, the role of cholesterol biosynthesis in Wilms tumor has not yet been well clarified. Methods RNA sequencing transcriptome data and all corresponding clinicopathological information used in our study were downloaded from the TARGET database. High-throughput sequencing (Fragments Per Kilobase of transcript per Million fragments mapped) data sets of 130 tumor samples and 6 normal samples were obtained for further analysis. Results Wilms tumor samples with higher activity of cholesterol synthesis are characterized with worse overall survival (P < 0.05). In addition, Wilms tumor samples with mitigated activity of cholesterol synthesis are featured with better dendritic cell (DC) function and cytolytic activity (P < 0.05). Furthermore, we constructed a prognosis model based on differential expressed cholesterol synthesis-related genes (DECSG), which could predict the OS of patients with Wilms tumor accurately. KEGG and GO analysis of differential expressed genes between tumor samples with high and low cholesterol synthesis indicated that DECSGs are highly enriched in “mitosis nuclear division,” “nuclear division,” “chromosome segregation,” “cell cycle,” “Spliceosome,” and “RNA transport.” Conclusions In conclusion, our study reported increased cholesterol synthesis in Wilms tumor predicts a worse prognosis and mitigated cytolytic activity, DC function, and MHC I signature in the tumor microenvironment. We also constructed a prognosis model for predicting the OS of patients with good accuracy, which is promising in clinical translation. Future studies should focus on the detailed mechanism that caused increasing cholesterol which promotes tumor progression and undermines patients' survival.

SurVirus: a repeat-aware virus integration caller

A significant portion of human cancers are due to viruses integrating into human genomes. Therefore, accurately predicting virus integrations can help uncover the mechanisms that lead to many devastating diseases. Virus integrations can be called by analysing second generation high-throughput sequencing datasets. Unfortunately, existing methods fail to report a significant portion of integrations, while predicting a large number of false positives. We observe that the inaccuracy is caused by incorrect alignment of reads in repetitive regions. False alignments create false positives, while missing alignments create false negatives. This paper proposes SurVirus, an improved virus integration caller that corrects the alignment of reads which are crucial for the discovery of integrations. We use publicly available datasets to show that existing methods predict hundreds of thousands of false positives; SurVirus, on the other hand, is significantly more precise while it also detects many novel integrations previously missed by other tools, most of which are in repetitive regions. We validate a subset of these novel integrations, and find that the majority are correct. Using SurVirus, we find that HPV and HBV integrations are enriched in LINE and Satellite regions which had been overlooked, as well as discover recurrent HBV and HPV breakpoints in human genome-virus fusion transcripts.

CDK12 inhibition enhances sensitivity of HER2+ breast cancers to HER2-tyrosine kinase inhibitor via suppressing PI3K/AKT

BackgroundAlhtough anti-HER2 tyrosine kinase inhibitors (TKIs) have radically prolonged survival and improved prognosis in HER2-positive breast cancer patients, resistance to these therapies is a constant obstacle leading to TKIs treatment failure and tumour progression. MethodsTo develop new strategies to enhance TKIs efficiency by combining synergistic gene targets, we performed panel library screening using the CRISPR/Cas9 knockout technique based on data mining across TCGA data sets and verified the candidate target in preclinical models and breast cancer high-throughput sequencing data sets. ResultsWe identified that CDK12, co-amplified with HER2 in a high frequency, is powerful to sensitise or resensitise HER2-positive breast cancer to anti-HER2 TKIs lapatinib, evidenced by patient-derived organoids in vitro and cell-derived xenograft or patient-derived xenograft in vivo. Exploring mechanisms, we found that inhibition of CDK12 attenuated PI3K/AKT signal, which usually serves as an oncogenic driver and is reactivated when HER2-positive breast cancers develop resistance to lapatinib. Combining CDK12 inhibition exerted additional suppression on p-AKT activation induced by anti-HER2 TKIs lapatinib treatment. Clinically, via DNA sequencing data for tumour tissue and peripheral blood ctDNA, we found that HER2-positive breast cancer patients with CDK12 amplification responded more insensitively to anti-HER2 treatment than those without accompanying CDK12 amplification by harbouring a markedly shortened progression-free survival (PFS) (median PFS: 4.3 months versus 6.9 months; hazards ratio [HR] = 2.26 [95% confidence interval [CI] = 1.32–3.86]; P = 0.0028). ConclusionsDual inhibition of HER2/CDK12 will prominently benefit the outcomes of patients with HER2-positive breast cancer by sensitising or resensitising the tumours to anti-HER2 TKIs treatment.

Potential Molecular Mechanisms and Remdesivir Treatment for Acute Respiratory Syndrome Corona Virus 2 Infection/COVID 19 Through RNA Sequencing and Bioinformatics Analysis.

Introduction:Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infections (COVID 19) is a progressive viral infection that has been investigated extensively. However, genetic features and molecular pathogenesis underlying remdesivir treatment for SARS-CoV-2 infection remain unclear. Here, we used bioinformatics to investigate the candidate genes associated in the molecular pathogenesis of remdesivir-treated SARS-CoV-2-infected patients.Methods:Expression profiling by high-throughput sequencing dataset (GSE149273) was downloaded from the Gene Expression Omnibus, and the differentially expressed genes (DEGs) in remdesivir-treated SARS-CoV-2 infection samples and nontreated SARS-CoV-2 infection samples with an adjusted P value of <.05 and a |log fold change| > 1.3 were first identified by limma in R software package. Next, pathway and gene ontology (GO) enrichment analysis of these DEGs was performed. Then, the hub genes were identified by the NetworkAnalyzer plugin and the other bioinformatics approaches including protein-protein interaction network analysis, module analysis, target gene—miRNA regulatory network, and target gene—TF regulatory network. Finally, a receiver-operating characteristic analysis was performed for diagnostic values associated with hub genes.Results:A total of 909 DEGs were identified, including 453 upregulated genes and 457 downregulated genes. As for the pathway and GO enrichment analysis, the upregulated genes were mainly linked with influenza A and defense response, whereas downregulated genes were mainly linked with drug metabolism—cytochrome P450 and reproductive process. In addition, 10 hub genes (VCAM1, IKBKE, STAT1, IL7R, ISG15, E2F1, ZBTB16, TFAP4, ATP6V1B1, and APBB1) were identified. Receiver-operating characteristic analysis showed that hub genes (CIITA, HSPA6, MYD88, SOCS3, TNFRSF10A, ADH1A, CACNA2D2, DUSP9, FMO5, and PDE1A) had good diagnostic values.Conclusion:This study provided insights into the molecular mechanism of remdesivir-treated SARS-CoV-2 infection that might be useful in further investigations.

α-Actinin1 promotes tumorigenesis and epithelial-mesenchymal transition of gastric cancer via the AKT/GSK3β/β-Catenin pathway

ABSTRACT α-Actinin1 (ACTN1), an actin cross-linking protein, is implicated in cytokinesis, cell adhesion, and cell migration. In addition, it is involved in the tumorigenesis and development of certain cancers, such as breast cancer. We explored the function of ACTN1 in gastric cancer (GC), which has largely remained unclear. High-throughput sequencing and public microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) revealed the upregulation of ACTN1 in gastric cancer with a poor prognosis. These results were further verified by western blotting (WB), Real-Time Quantitative polymerase chain reaction (RT-qPCR), and immunohistochemistry. We constructed loss and gain of function gastric cancer cells, which revealed the effect of ACTN1 over-expression on promoting GC cell proliferation, invasion, migration, and inhibited apoptosis. Mechanistic studies revealed that ACTN1 regulates the epithelial-mesenchymal transition (EMT) and tumorigenesis of gastric cancer via the AKT/GSK3β/β-catenin pathway, confirmed by the inhibitor of AKT MK2206. Altogether, these results demonstrated that ACTN1 could be a promising candidate for gastric cancer treatment.

Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines

Background:The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data.Results:We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets.Conclusions:AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra.

Data structures based on k-mers for querying large collections of sequencing data sets.

High-throughput sequencing data sets are usually deposited in public repositories (e.g., the European Nucleotide Archive) to ensure reproducibility. As the amount of data has reached petabyte scale, repositories do not allow one to perform online sequence searches, yet, such a feature would be highly useful to investigators. Toward this goal, in the last few years several computational approaches have been introduced to index and query large collections of data sets. Here, we propose an accessible survey of these approaches, which are generally based on representing data sets as sets of k-mers. We review their properties, introduce a classification, and present their general intuition. We summarize their performance and highlight their current strengths and limitations.

Biogeographic Distribution Patterns of Diatoms in Lancang River and Their Key Drivers

As an important primary producer, diatoms play a key role in aquatic ecosystems. However, little is known about the geographical distribution characteristics and driving factors of diatoms in large rivers. In this study, based on a high-throughput sequencing dataset of microeukaryotes, we analyzed the diversities and community compositions of planktonic and sedimentary diatoms in the 1200 km mainstream of Lancang River, a typical large river in southwestern China. The results showed that the diversities of planktonic and sedimentary diatoms in Lancang River were higher in the upstream natural section, and the community compositions of both groups were significantly different among different river sections. Dam construction had a significant effect on the dominant genera. Variance partitioning analysis showed that dispersal limitation was a major driving factor for the distribution pattern of planktonic and sedimentary diatoms, with explanation proportions of 16.7% and 29.8%. Co-occurrence network analyses showed that the interspecific competition relationship and network connectivity of the planktonic diatom network were stronger than the sedimentary ones. The network connectivity of planktonic and sedimentary diatoms in the cascade reservoir section was higher than that of the upstream natural section. This study will help to better understand the biogeographical distribution of diatoms in large rivers and provide useful information for ecological responses of diatoms to dam construction in rivers.

Proficiency Testing of Metagenomics-Based Detection of Food-Borne Pathogens Using a Complex Artificial Sequencing Dataset.

Metagenomics-based high-throughput sequencing (HTS) enables comprehensive detection of all species comprised in a sample with a single assay and is becoming a standard method for outbreak investigation. However, unlike real-time PCR or serological assays, HTS datasets generated for pathogen detection do not easily provide yes/no answers. Rather, results of the taxonomic read assignment need to be assessed by trained personnel to gain information thereof. Proficiency tests are important instruments of validation, harmonization, and standardization. Within the European Union funded project COMPARE [COllaborative Management Platform for detection and Analyses of (Re-) emerging and foodborne outbreaks in Europe], we conducted a proficiency test to scrutinize the ability to assess diagnostic metagenomics data. An artificial dataset resembling shotgun sequencing of RNA from a sample of contaminated trout was provided to 12 participants with the request to provide a table with per-read taxonomic assignments at species level and a report with a summary and assessment of their findings, considering different categories like pathogen, background, or contaminations. Analysis of the read assignment tables showed that the software used reliably classified the reads taxonomically overall. However, usage of incomplete reference databases or inappropriate data pre-processing caused difficulties. From the combination of the participants’ reports with their read assignments, we conclude that, although most species were detected, a number of important taxa were not or not correctly categorized. This implies that knowledge of and awareness for potentially dangerous species and contaminations need to be improved, hence, capacity building for the interpretation of diagnostic metagenomics datasets is necessary.

Netboxr: Automated discovery of biological process modules by network analysis in R.

Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.

Comparison of the diversity of cultured and total bacterial communities in marine sediment using culture-dependent and sequencing methods.

Despite recent great advances in microbial culture, most microbes have not yet been cultured, and the impact of medium composition on the isolation of microbes from natural systems has not been elucidated. To optimize media for culturing marine microbes, microbial communities in three sediment samples were described using high-throughput sequencing (HTS) and culture-dependent techniques. HTS revealed communities dominated by Gammaproteobacteria, and culture-based methods revealed communities dominated by Actinobacteria. Among the total operational taxonomic units (OTUs) from the HTS dataset, 6% were recovered in the culture collection. Four potentially novel bacterial strains belonging to Oceaniovalibus, Psychrobacter and Salegentibacter were isolated. The combination of media cultured more taxa than any single medium. Nutrient-rich and single-carbon/nitrogen-source media supported the growth of relatively few taxa, and the quality of nitrogen strongly influenced the types of bacteria isolated.

TRFTar: Prediction of tRF-target gene interactions via systemic re-analysis of Argonaute CLIP-seq datasets.

tRNA-derived fragments (tRFs), which by definition are cleaved from tRNAs, comprise a novel class of regulatory small non-coding RNAs. Recent evidence has revealed that tRFs can be loaded onto Argonaute (AGO) family proteins to perform post-transcriptional regulations via substantial tRF-target gene interactions (TGIs). However, there is no resource that systematically profiles potential AGO-mediated TGIs. To this end, we performed a systemic computational screening of potential AGO-mediated TGIs by a re-analysis of 146 crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) datasets in which 920,690 TGIs between 12,102 tRFs and 5,688 target genes were identified. The predicted TGIs have superior signal-to-noise ratio and good consistency with TGIs identified from an orthogonal technique. AGO-bound tRFs are not evenly distributed, where the 5'-tRF and 3'-tRF are enriched and some commonly expressed tRFs are also overrepresented. The tRFs tend to target conserved regions of transcripts and co-express with their target genes. Filtering TGIs with consistent co-expression with target genes results in a set of regulatory TGIs that contains 25,281 tRF-target pairs. Together, our results unveiled the extensive regulatory interactions between tRFs and target genes. Finally, the CLIP-derived TGIs were incorporated in a user-friendly online platform termed as tRFTar, where various functions like custom searching, co-expressed TGI filtering, genome browser and TGI-based tRF functional enrichment analysis are enabled to help users to investigate the functions of tRFs. The tRFTar is freely available at http://www.rnanut.net/tRFTar/.