A small bit of my thesis. Why are BMC PDFs so significantly larger on average than PLOS or Zootaxa PDFs?

data sources:

A) 'Zootaxa' the entire set of articles published in the journal Zootaxa from 2001 to 2012 inclusive, consisting of 11563 pdf files downloaded direct from the publisher website : http://mapress.com/zootaxa/ B) 'PLOS' the entire set of articles published across 7 different PLOS journals: PLOS ONE, PLOS Biology, PLOS Computational Biology, PLOS Genetics, PLOS Medicine, PLOS Neglected Tropical Diseases, and PLOS Pathogens from 2003 to 2010-06-04, consisting of 20694 articles obtained via BioTorrents (Langille & Eisen, 2010). C) 'BMC' a subsample of 7948 open access articles containing the stemword 'phylogen*' at least once in the fulltext from the wide range of journals that BioMedCentral publish (the OA subset of this selection of papers: http://www.citeulike.org/user/testtest87)

Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub “stars,” “watchers,” and “forks” (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.

BackgroundRecently, increasing studies have shown that non-coding RNAs are closely associated with the progression and metastasis of cancer by participating in competing endogenous RNA (ceRNA) networks. However, the role of survival-associated ceRNAs in breast cancer (BC) remains unknown.MethodsThe Gene Expression Omnibus database and The Cancer Genome Atlas BRCA_dataset were used to identify differentially expressed RNAs. Furthermore, circRNA-miRNA interactions were predicted based on CircInteractome, while miRNA-mRNA interactions were predicted based on TargetScan, miRDB, and miRTarBase. The survival-associated ceRNA networks were constructed based on the predicted circRNA-miRNA and miRNA-mRNA pairs. Finally, the mechanism of miRNA-mRNA pairs was determined. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of survival-related mRNAs were performed using the hypergeometric distribution formula in R software.The prognosis of hub genes was confirmed using gene set enrichment analysis.ResultsBased on the DE-circRNAs of the top 10 initial candidates, 162 DE-miRNAsand 34 DE-miRNAs associated with significant overall survival were obtained. The miRNA target genes were then identified using online tools and verified using the Cancer Genome Atlas (TCGA) database. Overall, 46 survival-associated DE-mRNAs were obtained. The results of GO and KEGG pathway enrichment analyses implied that up-regulated survival-related DE-mRNAs were mostly enriched in the “regulation of cell cycle” and “chromatin” pathways, while down-regulated survival-related DE-mRNAs were mostly enriched in “negative regulation of neurotrophin TRK receptor signaling” and “interleukin-6 receptor complex” pathways. Finally, the survival-associated circRNA-miRNA-mRNA ceRNA network was constructed using 34 miRNAs, 46 mRNAs, and 10 circRNAs. Based on the PPI network, two ceRNA axes were identified. These ceRNA axescould be considered biomarkers for BC.GSEA results revealed that the hub genes were correlated with “VANTVEER_BREAST_CANCER_POOR_PROGNOSIS”, and the hub genes were verified using BC patients' tissues.ConclusionsIn this study, we constructed a circRNA-mediated ceRNA network related to BC. This network provides new insight into discovering potential biomarkers for diagnosing and treating BC.

Assay for transposase-accessible chromatin using sequencing data (ATAC-seq) is an efficient and precise method for revealing chromatin accessibility across the genome. Most of the current ATAC-seq tools follow chromatin immunoprecipitation sequencing (ChIP-seq) strategies that do not consider ATAC-seq-specific properties. To incorporate specific ATAC-seq quality control and the underlying biology of chromatin accessibility, we developed a bioinformatics software named ATACgraph for analyzing and visualizing ATAC-seq data. ATACgraph profiles accessible chromatin regions and provides ATAC-seq-specific information including definitions of nucleosome-free regions (NFRs) and nucleosome-occupied regions. ATACgraph also allows identification of differentially accessible regions between two ATAC-seq datasets. ATACgraph incorporates the docker image with the Galaxy platform to provide an intuitive user experience via the graphical interface. Without tedious installation processes on a local machine or cloud, users can analyze data through activated websites using pre-designed workflows or customized pipelines composed of ATACgraph modules. Overall, ATACgraph is an effective tool designed for ATAC-seq for biologists with minimal bioinformatics knowledge to analyze chromatin accessibility. ATACgraph can be run on any ATAC-seq data with no limit to specific genomes. As validation, we demonstrated ATACgraph on human genome to showcase its functions for ATAC-seq interpretation. This software is publicly accessible and can be downloaded at https://github.com/RitataLU/ATACgraph.

BackgroundsCuprotosis is a newly discovered programmed cell death by modulating tricarboxylic acid cycle. Emerging evidence showed that cuprotosis-related genes (CRGs) are implicated in the occurrence and progression of multiple diseases. However, the mechanism of cuprotosis in heart failure (HF) has not been investigated yet.MethodsThe HF microarray datasets GSE16499, GSE26887, GSE42955, GSE57338, GSE76701, and GSE79962 were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed CRGs between HF patients and nonfailing donors (NFDs). Four machine learning models were used to identify key CRGs features for HF diagnosis. The expression profiles of key CRGs were further validated in a merged GEO external validation dataset and human samples through quantitative reverse-transcription polymerase chain reaction (qRT-PCR). In addition, Gene Ontology (GO) function enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and immune infiltration analysis were used to investigate potential biological functions of key CRGs.ResultsWe discovered nine differentially expressed CRGs in heart tissues from HF patients and NFDs. With the aid of four machine learning algorithms, we identified three indicators of cuprotosis (DLAT, SLC31A1, and DLST) in HF, which showed good diagnostic properties. In addition, their differential expression between HF patients and NFDs was confirmed through qRT-PCR. Moreover, the results of enrichment analyses and immune infiltration exhibited that these diagnostic markers of CRGs were strongly correlated to energy metabolism and immune activity.ConclusionsOur study discovered that cuprotosis was strongly related to the pathogenesis of HF, probably by regulating energy metabolism-associated and immune-associated signaling pathways.

Breast cancer (BC) has high morbidity, with significant relapse and mortality rates in women worldwide. Therefore, further exploration of its pathogenesis is of great significance. This study selected therapy genes and possible biomarkers to predict BC using bioinformatic methods. To this end, the study examined 21 healthy breasts along with 457 BC tissues in two Gene Expression Omnibus (GEO) datasets and then identified differentially expressed genes (DEGs). Survival-associated DEGs were screened using the Kaplan–Meier curve. Based on Gene Ontology (GO) annotation, survival-associated DEGs were mostly associated with cell division and cellular response to hormone stimulus. The enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathway was mostly correlated with cell cycle and tyrosine metabolism. Using overlapped survival-associated DEGs, a survival-associated PPI network was constructed. PPI analysis revealed three hub genes (EZH2, CCNB1, and PPARG) by their degree of connection. These hub genes were confirmed using The Cancer Genome Atlas (TCGA)-BRCA dataset and BC tissue samples. Through Gene Set Enrichment Analysis (GSEA), the molecular mechanism of the potential therapy and prognostic genes were evaluated. Thus, hub genes were shown to be associated with KEGG_CELL_CYCLE and VANTVEER_BREAST_CANCER_POOR_PROGNOSIS gene sets. Finally, based on integrated bioinformatics analysis, this study identified three hub genes as possible prognostic biomarkers and therapeutic targets for BC. The results obtained further understanding of the underground molecular mechanisms related to BC occurrence and prognostic outcomes.

Markdown source, PDF, and HTML rendering of bioinformatics training resources from http://stephenturner.us/p/edu.

BackgroundThe exact mechanisms of PD are unclear, but Parkin-mediated mitophagy dysfunction is believed to play a key role. We investigated whether blood levels of Parkin and other biomarkers are linked to the risk of developing PD.MethodsBaseline blood measures of Parkin and other biomarkers, including Homocysteine, carcinoembryonic antigen, Urea, total proteins, total cholesterol, creatine kinase, and albumin, were collected from 197 clinically diagnosed Parkinson’s disease participants and 107 age-matched healthy controls in Wenzhou Parkinson’s Biomarkers and Living Characteristics study. We conducted bioinformatics analysis using three datasets from the GEO database: GSE90514 (Cohort 1: PD = 4, HC = 4), GSE7621 (Cohort 2: PD = 16, HC = 9), and GSE205450 (Cohort 3: PD = 69, HC = 81).ResultsUsing a bioinformatic approach, we identified dysregulated biological processes in PD patients with PRKN mutations. Compared to controls, significant abnormalities were observed in blood levels of Parkin, Hcy, total proteins, urea, albumin, and CEA in PD patients. A model incorporating Parkin, Hcy, total proteins, and urea effectively distinguished PD from healthy controls, achieving a higher accuracy (AUC 0.841) than other biomarker combinations. Gene set enrichment analysis suggested that pathways such as PINK1-Parkin-mediated mitophagy, urea cycle, cysteine degradation, and riboflavin metabolism may be involved in PRKN mutation. Additionally, the link between Parkin and PD was partially mediated by CEA and albumin, not by Hcy, total proteins, or urea.ConclusionOur findings indicate that blood Parkin levels may be a minimally invasive biomarker for PD diagnosis. The model, which included Parkin, Hcy, total proteins, and urea, effectively distinguished PD from HC with greater accuracy.

Diffuse large B-cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) are subtypes of non-Hogkin lymphoma (NHL) that are generally distinct form one cases, but the transformation of one of these diseases into the other is possible. Some patients with CLL, for instance, have the potential to develop Richter transformation such that they are diagnosed with a rare, invasive DLBCL subtype. In this study, bioinformatics analyses of these two NHL subtypes were conducted, identifying key patterns of gene expression and then experimentally validating the results. Disease-related gene expression datasets from the GEO database were used to identify differentially expressed genes (DEGs) and DEG functions were examined using GO analysis and protein-protein interaction network construction. This strategy revealed many up- and down-regulated DEGs, with functional enrichment analyses identifying these genes as being closely associated with inflammatory and immune response activity. PPI network analyses and the evaluation of clustered network modules indicated the top 10 up- and down-regulated genes involved in disease onset and development. Serological analyses revealed significantly higher ALB, TT, and WBC levels in CLL patients relative to DLBCL patients, whereas the opposite was true with respect to TG, HDL, GGT, ALP, ALT, and NEUT% levels. In comparison to the CLL and DLBCL groups, the healthy control samples demonstrated higher signals of protein peak positions (621, 643, 848, 853, 869, 935, 1003, 1031, 1221, 1230, 1260, 1344, 1443, 1446, 1548, 1579, 1603, 1647 cm-1), nucleic acid peak positions (726, 781, 786, 1078, 1190, 1415, 1573, 1579 cm-1), beta carotene peak positions (957, 1155, 1162 cm-1), carbohydrate peak positions (842 cm-1), collagen peak positions (1345 cm-1), and lipid peak positions (957, 1078, 1119, 1285, 1299, 1437, 1443, 1446 cm-1) compared to the CLL and DLBCL groups. Verification of these key genes in patient samples yielded results consistent with findings derived from bioinformatics analyses, highlighting their relevance to diagnosing and treating these forms of NHL. Together, these analyses identified genes and pathways involved in both DLBCL and CLL. The set of molecular markers established herein can aid in patient diagnosis and prognostic evaluation, providing a valuable foundation for their therapeutic application.

Bioinformatics is a highly interdisciplinary subject, with substantial and growing influence in health, environmental science and society, and is utilised by scientists from many diverse academic backgrounds. Education in bioinformatics therefore necessitates effective development of skills in interdisciplinary collaboration, communication, ethics, and critical analysis of research, in addition to practical and technical skills. Insights from bioinformatics training can additionally inform developing education in the tightly aligned and emerging disciplines of data science and artificial intelligence. Here, we describe the design, implementation, and review of a module in a UK MSc-level bioinformatics programme attempting to address these goals for diverse student cohorts. Reflecting the philosophy of the field and programme, the module content was designed either as “diversity-addressing”—working toward a common foundation of knowledge—or “diversity-exploiting”—where different student viewpoints and skills were harnessed to facilitate student research projects “greater than the sum of their parts.” For a universal introduction to technical concepts, we combined a mixed lecture/immediate computational practical approach, facilitated by virtual machines, creating an efficient technical learning environment praised in student feedback for building confidence among cohorts with diverse backgrounds. Interdisciplinary group research projects where diverse students worked on real research questions were supervised in tandem with interactive contact time covering transferable skills in collaboration and communication in diverse teams, research presentation, and ethics. Multi-faceted feedback and assessment provided a constructive alignment with real peer-reviewed bioinformatics research. We believe that the inclusion of these transferable, interdisciplinary, and critical concepts in a bioinformatics course can help produce rounded, experienced graduates, ready for the real world and with many future options in science and society. In addition, we hope to provide some ideas and resources to facilitate such inclusion.

FBLN5, a member of the short fibulins in the fibulin family of extracellular matrix/matricellular proteins, is involved in interactions with components of the basement membrane and extracellular matrix proteins. It plays key roles in endothelial tissues in many vascular diseases. In this study, the relationship between FBLN5 and carotid atherosclerotic plaque stability as well as the regulatory roles of miRNAs were evaluated. Differential gene expression analyses and weighted gene co-expression network analysis (WGCNA) based on the GSE163154 dataset (including 16 samples without intraplaque hemorrhage and 27 samples with intraplaque hemorrhage) in GEO revealed that FBLN5 is related to plaque stability and is the most significantly differentially expressed gene. LASSO regression was used to evaluate genes obtained from the intersection of differentially expressed genes and clinically significant modules identified by WGCNA. A prediction model based on eight genes, including FBLN5, was constructed and showed an accuracy of 0.951 based on an ROC analysis. Low FBLN5 expression in plaque tissues was confirmed by immunohistochemistry and western blotting. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analyses showed that FBLN5 acted mainly by the maintenance of the cellular matrix and reactive oxygen species production. miRNAs upstream of these eight predictive genes, including FBLN5, were identified and used to construct a network diagram. These results revealed that hsa-mir-128 and hsa-mir-532–3p were upstream regulatory factors of FBLN5, as verified by PCR assays of human plaque tissues demonstrating that both miRNAs were significantly up-regulated. Therefore, FBLN5 may play an important role in carotid atherosclerosis via hsa-mir-128 and hsa-mir-532–3p as well as become an essential target for treatment.

The molecular mechanisms underlying obesity-related cardiomyopathy (ORCM) progression involve multiple signaling pathways, and the pharmacological treatment for ORCM is still limited. Thus, it is necessary to explore new targets and develop novel therapies. Microarray analysis for gene expression profiles using different bioinformatics tools has been an effective strategy for identifying novel targets for various diseases. In this study, we aimed to explore the potential genes related to ORCM using the integrated bioinformatics analysis. The GSE18897 (whole blood expression profiling of obese diet-sensitive, obese diet-resistant, and lean human subjects) and GSE47022 (regular weight C57BL/6 and diet-induced obese C57BL/6 mice) were used for bioinformatics analysis. Weighted gene co-expression network analysis (WGCNA) of GSE18897 was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the co-expression genes. The expression levels of the hub genes were validated in the clinical samples. Yellow co-expression module of WGCNA in GSE18897 was found to be significantly related to the caloric restriction treatment. In addition, GO functional enrichment analysis and KEGG pathway analysis were performed on the co-expression genes in yellow co-expression module, which showed an association with oxygen transport and the porphyrins pathway. Overlap analysis of yellow co-expression module genes from GSE18897 andGSE47022 revealed six upregulated genes, and further experimental validation results showed that elongation of very-long-chain fatty acids protein 4 (ELOVL4), matrix metalloproteinase-8 (MMP-8), and interleukin-33 (IL-33) were upregulated in the peripheral blood from patients with ORCM compared to that in the controls. The bioinformatics analysis revealed that ELOVL4 expression levels are positively correlated with that of IL-33. Collectively, using WGCNA in combination with integrated bioinformatics analysis, the hub genes of ELVOL4 and IL-33 might serve as potential biomarkers for diagnosis and/or therapeutic targets for ORCM. The detailed roles of ELVOL4 and IL-33 in the pathophysiology of ORCM still require further investigation.

BackgroundUC patients suffer more from colorectal cancer (CRC) than the general population, which increases with disease duration. Early colonoscopy is difficult because ulcerative colitis-associated colorectal cancer (UCAC) lesions are flat and multifocal. Our study aimed to identify promising UCAC biomarkers that are complementary endoscopy strategies in the early stages.MethodsThe datasets may be accessed from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The co-expressed modules of UC and CRC were determined via weighted co-expression network analysis (WGCNA). The biological mechanisms of the shared genes were exported for analysis using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. To identify protein interactions and hub genes, a protein-protein interaction network and CytoHubba analysis were conducted. To evaluate gene expression, external datasets and experimental validation of human colon tissues were utilized. The diagnostic value of core genes was examined through receiver operating characteristic (ROC) curves. Immune infiltration analysis was employed to investigate the associations between immune cell populations and hub genes.ResultsThree crucial modules were identified from the WGCNA of UC and CRC tissues, and 33 coexpressed genes that were predominantly enriched in the NF-κB pathway were identified. Two biomarkers (CXCL1 and BCL6) were identified via Cytoscape and validated in external datasets and human colon tissues. CRC patients expressed CXCL1 at the highest level, whereas UC and CRC patients showed higher levels than the controls. The UC cohort expressed BCL6 at the highest level, whereas the UC and CRC cohorts expressed it more highly than the controls. The hub genes exhibited significant diagnostic potential (ROC curve > 0.7). The immune infiltration results revealed a correlation among the hub genes and macrophages, neutrophils and B cells.ConclusionsThe findings of our research suggest that BCL6 and CXCL1 could serve as effective biomarkers for UCAC surveillance. Additionally, they demonstrated a robust correlation with immune cell populations within the CRC tumour microenvironment (TME). Our findings provide a valuable insight about diagnosis and therapy of UCAC.

Phylogenetic re-analyses of Insulin-like Growth Factor Binding Proteins (IGFBPs) based on amino acid sequences. The sequences and alignment described in Ocampo Daza et al. (2011) Endocrinology 152(6):2278-89 (link below) were used to analyze additional IGFBP sequences identified in the genome databases of Anolis carolinensis (anole lizard), Latimeria chalumnae (coelacanth) and Lepisosteus oculatus (spotted gar). Phylogenetic trees were made using neighbor joining (NJ) and phylogenetic maximum likelihood (PhyML) methods, both supported by bootstrap analyses (details below). Figures (PDF-files) of the finished trees are included in the files IGFBP_NJ_figure.pdf and IGFBP_PhyML_figure.pdf. Branch colors are based on chromosomal locations and follow the trees published in Ocampo Daza et al. (2011) (link below). Species abbreviations Homo sapiens (Hsa, human), Mus musculus (Mmu, mouse), Canis familiaris (Cfa, dog), Monodelphis domestica (Mdo, opossum), Gallus gallus (Gga, chicken), Taeniopygia guttata (Tgu, zebra finch), Anolis carolinensis (Aca, anole lizard), Latimeria chalumnae (Lch, coelacanth), Lepisosteus oculatus (Loc, spotted gar), Danio rerio (Dre, zebrafish), Oryzias latipes (Ola, medaka),Gasterosteus aculeatus (Gac, stickleback), Tetraodon nigroviridis (Tni, green-spotted pufferfish),Takifugu rubripes (Tru, fugu), Ciona intestinalis (Cin, vase tunicate), Ciona savignyi (Csa, Pacific transparent tunicate) and Branchiostoma floridae (Bfl, Florida lancelet). Sequences used Detailed information about all sequences that were used is included in the file Sequence_info_Tab1.xlsx (MS Excel spreadsheet). This includes database identifiers and chromosome/linkage group locations as well as notes on the manual curation/annotation of the sequences. Alignment The full amino acid sequence alignment used for the phylogenetic analyses is included in an interleaved format (.aln) and a sequential format (.fasta) in the files IGFBP_alignment_interleaved.aln and IGFBP_alignment_sequential.fasta. The alignment was made using the ClustalW algorithm and edited manually as described in Ocampo Daza et al. (2011) Endocrinology 152(6):2278-89 (link below). Anole lizard, coelacanth and spotted gar sequences marked with asterisks are fragments and do not span the full length of the alignment (details in the file Sequence_info_Tab1.xlsx). Phylogenetic analysis, NJ method The Neighbor Joining tree was made in ClustalX 2.0, with settings as described in Ocampo Daza et al. (2011) (link below). The tree is supported by a bootstrap analysis with 1000 bootstrap replicates. The raw output is included in the file IGFBP_NJ.txt and the final tree, rooted with the lancelet IGFBP sequence, is included in the file IGFBP_NJ_rooted.phb. Both files are in the Newick/Phylip data format. Phylogenetic trees, PhyML method The Phylogenetic Maximum Likelihood tree was made using the PhyML3.0 algorithm implemented through the web-based interface available at http://www.atgc-montpellier.fr/phyml/. The following settings were used: . Amino acid subst. model : LG. Proportion of invariable sites : estimated. Number of subst. rate categs : 8. Gamma distribution parameter : estimated. 'Middle' of each rate class : mean. Amino acid equilibrium frequencies : empirical. Optimise tree topology : yes. Tree topology search : NNIs. Starting tree : BioNJ. Add random input tree : no. Optimise branch lengths : yes. Optimise substitution model parameters : yes The tree is supported by a bootstrap analysis with 100 bootstrap replicates. The final tree, rooted with the lancelet IGFBP sequence, is included in the file IGFBP_PhyML.phb (Newick/Phylip format). The raw output files of the PhyML analysis are included in the following files: . igfbp_ml_121119_phy_stdout.txt . igfbp_ml_121119_phy_phyml_tree.txt . igfbp_ml_121119_phy_phyml_stats.txt . igfbp_ml_121119_phy_phyml_boot_trees.txt . igfbp_ml_121119_phy_phyml_boot_stats File formats All phylogenetic data is included in the Newick/Phylip format. For more information on the PhyML output files and data formats, see http://www.atgc-montpellier.fr/download/papers/phyml_manual_2009.pdf.

COVID-19 manifests a spectrum of respiratory symptoms, with the more severe often requiring hospitalization. To identify markers for disease progression, we analyzed longitudinal gene expression data from patients with confirmed SARS-CoV-2 infection admitted to the intensive care unit (ICU) for acute hypoxic respiratory failure (AHRF) as well as other ICU patients with or without AHRF and correlated results of gene set enrichment analysis with clinical features. The results were then compared with a second dataset of COVID-19 patients separated by disease stage and severity. Transcriptomic analysis revealed that enrichment of plasma cells (PCs) was characteristic of all COVID-19 patients whereas enrichment of interferon (IFN) and neutrophil gene signatures was specific to patients requiring hospitalization. Furthermore, gene expression results were used to divide AHRF COVID-19 patients into 2 groups with differences in immune profiles and clinical features indicative of severe disease. Thus, transcriptomic analysis reveals gene signatures unique to COVID-19 patients and provides opportunities for identification of the most at-risk individuals.

Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.

supplemental_file_list.pdf-- contains a list of all supplemental files. supplemental_materials.pdf-- contains supplemental figures, tables, and references. at_risk_id_key.tsv-- contains phenotype information for at-risk dogs in this study. breed_id_key.tsv-- contains breed and sex information for breed dogs in this study. at_risk.ped.gz contains genotype data (gzipped) in PLINK .ped format for at-risk dogs in this study.at_risk.map contains marker location information in PLINK .map format for at-risk dogs in this study. breeds.ped.gz contains genotype data (gzipped) in PLINK .ped format for dogs in the breed analysis in this study.breeds.map contains marker location information in PLINK .map format for dogs in the breed analysis in this study. postprocess_germline.py contains a python (v2) script for postprocessing germline homozygosity tracts generated using the flags presented in Materials & Methods.

Dataset used to generate figure 6 and 7.Figure 6: Analysis of human breast cancer (Block A Section 1), from 10XGenomics Visium Spatial Gene Expression 1.0.0. demonstration samples. A) SIMLR partitioning in 9 clusters (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_Stability_Plot.pdf). B) Cell stability score plot for SIMLR clusters in A (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_Stability_Plot.pdf. C) SIMLR clusters location in the tissue section (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_spatial_Stability.pdf). D) Hematoxylin and eosin image (figure6and7/HBC_BAS1/spatial/V1_Breast_Cancer_Block_A_Section_1_image.tif).Figure 6: Analysis of human breast cancer (Block A Section 1), from 10XGenomics Visium Spatial Gene Expression 1.0.0. demonstration samples. A) SIMLR partitioning in 9 clusters (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_Stability_Plot.pdf). B) Cell stability score plot for SIMLR clusters in A (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_Stability_Plot.pdf. C) SIMLR clusters location in the tissue section (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_BAS1_expr-var-ann_matrix_spatial_Stability.pdf). D) Hematoxylin and eosin image (figure6and7/HBC_BAS1/spatial/V1_Breast_Cancer_Block_A_Section_1_image.tif).Figure 7: Information contents extracted by SCA analysis using a TF-based latent space. A) QCC (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_TF_SIMLRV2/9/HBC_BAS1_expr-var-ann_matrix_stabilityPlot.pdf). B) QCM (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_TF_SIMLRV2/9/HBC_BAS1_expr-var-ann_matrix_stabilityPlotUNBIAS.pdf). C) QCM/QCC plot, where only cluster 7 show, for the majority of the cells, both QCC and QCM greater than 0.5 (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/HBC_TF_SIMLRV2/9/HBC_BAS1_expr-var-ann_matrix_StabilitySignificativityJittered.pdf). D) COMET analysis of SCA latent space. SOX5 was detected as first top ranked gene specific for cluster 7, using as input for COMET the latent space frequency table (figure6and7/HBC_BAS1/Results_simlr/raw-counts/HBC_BAS1_expr-var-ann_matrix/9/outputvis/cluster_7_singleton/rank_1.png). Input counts table for SCA analysis is made by raw counts.

Mouse line BTBR T+ Iptr3tf/J (hereafter referred as to BTBR/J) is a mouse strain that shows lower sociability compared to the C57BL/6J mouse strain (B6) and thus is often utilized as a model for autism spectrum disorder (ASD). In this study, we utilized another subline, BTBRTF/ArtRbrc (hereafter referred as to BTBR/R), and analyzed the associated brain transcriptome compared to B6 mice using microarray analysis, quantitative RT-PCR analysis, various bioinformatics analyses, and in situ hybridization. We focused on the cerebral cortex and the striatum, both of which are thought to be brain circuits associated with ASD symptoms. The transcriptome profiling identified 1,280 differentially expressed genes (DEGs; 974 downregulated and 306 upregulated genes, including 498 non-coding RNAs [ncRNAs]) in BTBR/R mice compared to B6 mice. Among these DEGs, 53 genes were consistent with ASD-related genes already established. Gene Ontology (GO) enrichment analysis highlighted 78 annotations (GO terms) including DNA/chromatin regulation, transcriptional/translational regulation, intercellular signaling, metabolism, immune signaling, and neurotransmitter/synaptic transmission-related terms. RNA interaction analysis revealed novel RNA–RNA networks, including 227 ASD-related genes. Weighted correlation network analysis highlighted 10 enriched modules including DNA/chromatin regulation, neurotransmitter/synaptic transmission, and transcriptional/translational regulation. Finally, the behavioral analyses showed that, compared to B6 mice, BTBR/R mice have mild but significant deficits in social novelty recognition and repetitive behavior. In addition, the BTBR/R data were comprehensively compared with those reported in the previous studies of human subjects with ASD as well as ASD animal models, including BTBR/J mice. Our results allow us to propose potentially important genes, ncRNAs, and RNA interactions. Analysis of the altered brain transcriptome data of the BTBR/R and BTBR/J sublines can contribute to the understanding of the genetic underpinnings of autism susceptibility.

BackgroundType 2 diabetes (T2DM) combined with fatty liver is a subtype of metabolic fatty liver disease (MAFLD), and the relationship between T2DM and MAFLD is close and mutually influential. However, the connection and mechanisms between the two are still unclear. Therefore, we aimed to identify potential biomarkers for diagnosing both conditions.MethodsWe performed differential expression analysis and weighted gene correlation network analysis (WGCNA) on publicly available data on the two diseases in the Gene Expression Omnibus database to find genes related to both conditions. We utilised protein–protein interactions (PPIs), Gene Ontology, and the Kyoto Encyclopedia of Genes and Genomes to identify T2DM-associated MAFLD genes and potential mechanisms. Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. Finally, we collected whole blood from patients with T2DM-related MAFLD, MAFLD patients and healthy individuals, and used high-fat, high-glucose combined with high-fat cell models to verify the expression of hub genes.ResultsDifferential expression analysis and WGCNA identified 354 genes in the MAFLD dataset. The differential expression analysis of the T2DM-peripheral blood mononuclear cells/liver dataset screened 91 T2DM-associated secreted proteins. PPI analysis revealed two important modules of T2DM-related pathogenic genes in MAFLD, which contained 49 nodes, suggesting their involvement in cell interaction, inflammation, and other processes. TNFSF10, SERPINB2, and TNFRSF1A were the only coexisting genes shared between MAFLD key genes and T2DM-related secreted proteins, enabling the construction of highly accurate diagnostic models for both disorders. Additionally, high-fat, high-glucose combined with high-fat cell models were successfully produced. The expression patterns of TNFRSF1A and SERPINB2 were verified in patient blood and our cellular model. Immune dysregulation was observed in MAFLD, with TNFRSF1A and SERPINB2 strongly linked to immune regulation.ConclusionThe sensitivity and accuracy in diagnosing and predicting T2DM-associated MAFLD can be greatly improved using SERPINB2 and TNFRSF1A. These genes may significantly influence the development of T2DM-associated MAFLD, offering new diagnostic options for patients with T2DM combined with MAFLD.