Human RNA-Seq data set GSM2819694 stored in NCBI (GEO)

Human RNA-Seq data set GSM2819698 stored in NCBI (GEO)

liver tissue sample : 6922_IZ_RNA

Human RNA-Seq data set GSM2819696 stored in NCBI (GEO)

liver tissue sample : 6610_IZ_RNA

Human DNA methylation data stored in NCBI (GEO) Dataset GSM2819623;

liver tissue sample** 6967_ CV_ RRBS** in SEEK https://seek.lisym.org/samples/133

Human DNA methylation data set GSM2819637 stored in NCBI (GEO)

We analysed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

- This release includes GEO series published up to Dec-31, 2020;

geo-htseq.tar.gz archive contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series.

- output/suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions.

- output/suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO.

- output/publications.csv, publication info of NCBI GEO series.

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/spots.csv, NCBI SRA sequencing run metadata.

- output/cancer.csv, cancer related experiment accessions.

- output/transcription_factor.csv, TF related experiment accessions.

- output/single-cell.csv, single cell experiment accessions.

- blacklist.txt, list of supplementary files that were either too large to import or were causing computing environment crash during import.

Workflow to produce this dataset is available on Github at rstats-tartu/geo-htseq.

geo-htseq-updates.tar.gz archive contains files:

- results/detools_from_pmc.csv, differential expression analysis programs inferred from published articles

- results/n_data.csv, manually curated sample size info for NCBI GEO HT-seq series

- results/simres_df_parsed.csv, pi0 values estimated from differential expression results obtained from simulated RNA-seq data

- results/data/parsed_suppfiles_rerun.csv, pi0 values estimated using smoother method from anti-conservative p-value sets

We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series

- output/publications.csv, publication info of NCBI GEO series

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, NCBI SRA sequencing run metadata

- suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

We introduce a new high-throughput transcriptomics (HTTr) platform comprised of a collagen sandwich primary rat hepatocyte culture and the TempO-Seq assay for screening and prioritizing potential hepatotoxicants. We selected 14 chemicals based on their risk of drug-induced liver injury (DILI) and tested them in hepatocytes at two treatment concentrations. HTTr data was generated using the TempO-Seq whole transcriptome and S1500+ assays. The HTTr platform exhibited high reproducibility between technical replicates (r>0.9) but biological replication was greater for TempO-Seq S1500+ (r>0.85) than for the whole transcriptome (r>0.7). Reproducibility between biological replicates was dependent on the strength of transcriptional effects induced by a chemical treatment. Despite targeting a smaller number of genes, the S1500+ assay clustered chemical treatments and produced gene set enrichment analysis (GSEA) scores comparable to those of the whole transcriptome. Connectivity mapping showed a high-level of reproducibility between TempO-Seq data and Affymetrix GeneChip data from the Open TG-GATES project with high concordance between the S1500+ gene set and whole transcriptome. Taken together, our results provide guidance on selecting the number of technical and biological replicates and support the use of TempO-Seq S1500+ assay for a high-throughput platform for screening hepatotoxicants. FASTQ files and read counts data have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GEO) (GSE152128). This dataset is associated with the following publication: Lee, F., I. Shah, Y.T. Soong, J. Xing, I.C. Ng, F. Tasnim, and H. Yu. Reproducibility and Robustness of High-Throughput S1500+ Transcriptomics on Primary Rat Hepatocytes for Chemical-Induced Hepatotoxicity Assessment. Current Research in Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 2: 282-295, (2021).

HumanDNA methylation data set stored in NCBI (GEO) Data set GSM2819620;

GSM2819620_CV_6610_207 7344_PP_RNA; Homo sapiens; RNA-Seq SEEK Sample: 6610_CV_RRBS

[https://seek.lisym.org/samples/130 ]

This link contains 3 files:

GSM2819620_CV_6610_207.MCSv3.20161129.hs37.cpg.filtered.CG.bed.gz 109.4 Mb (ftp)(http) BED GSM2819620_CV_6610_207.MCSv3.20161129.hs37.cpg.filtered.CG.bw 85.6 Mb (ftp)(http) BW GSM2819620_CV_6610_207.MCSv3.20161129.hs37.cpg.filtered.CG.ct_coverage.bw 86.8 Mb (ftp)(http) BW

Modern humans spend most of their time having eaten recently. The purpose of the current project is to understand how the blood, which contains immune cells, responds in the hours after eating a meal that is moderately high in fat. We used a sequencing method to observe the expression of all the genes in blood cells in five participants who were each fed a high fat meal on three separate days. The results are reported in the manuscript, “Temporal changes in postprandial blood transcriptomes reveal subject-specific pattern of expression of innate immunity genes after a high-fat meal." Overall design: We used a sequencing method to observe the expression of all the genes in blood cells in five participants who were each fed a high fat meal on three separate days, resulting in 45 whole blood transcriptomes. For each sample, 3 mL of venous whole blood was drawn into a Tempus Blood RNA tube, shaken vigorously, and then frozen at -80°C until use. Total RNA was purified with the Tempus Spin RNA Isolation Kit with minor modifications to the manufacturer’s protocol. To remove residual genomic DNA, RNA samples were treated on-column with RNase-Free DNase per manufacturer’s instructions. RNA quantity, quality, and integrity were assessed with NanoDrop 1000 and 2100 Bioanalyzer. All isolated RNA had A260/A280 ratios greater than 2 and RNA integrity numbers higher than 7.3. RNA-Seq libraries were constructed at the DNA Technologies and Expression Core at the University of California, Davis, using the Ovation Human Blood RNA-Seq Library System (NuGEN Technologies). Sequencing was performed in a 2x100bp format with 45 samples multiplexed on 3 lanes on an Illumina HiSeq 4000. Analysis of the data is reported in the manuscript, “Temporal changes in postprandial blood transcriptomes reveal subject-specific pattern of expression of innate immunity genes after a high-fat meal.”

This dataset provides a comprehensive pipeline for automated retrieval of gene expression data.

Supports both Microarray and RNA-Seq datasets from the NCBI GEO database.

Designed for researchers and bioinformaticians to streamline GEO data analysis.

Includes R scripts to download, process, and export GEO datasets efficiently.

Handles metadata extraction, sample annotation, and expression matrix generation.

Facilitates downstream analyses such as differential gene expression and visualization.

Compatible with various GEO platforms, reducing manual data curation efforts.

Enables reproducible research by standardizing data retrieval and processing.

Useful for comparative studies, functional genomics, and biomarker discovery.

Reduces the technical barrier for users unfamiliar with GEO data structures.

Context

No matter how much you wash your hands, you are still susceptible to flu airborne viruses or cold viruses in close proximity to others who have a cold or flu. The flu vaccine is a treatment many folks get in hopes of not getting sick that cold/flu season. The flu vaccine is somewhat of a math cheat sheet for your body preparing for a math course final without having to know all of the formulas off hand, but only the ones that are on the exam. If you have a crooked teacher/TA that decided not to allow the cheat sheet to be a good representation of what the content of the final exam is, then you could assume that is how your body will be with a flu vaccine that doesn't have the strand(s) of flu your body is likely to encounter that flu season. I found this data set munging the GEO database sets of NCBI while searching for 'flu vaccines' and wanted some microarray gene expression data sets that I could also compare those values to other blood micro array samples from separate studies on females using EGCG for obesity, and males who do/don't have heart disease. This data can be blended with the other data sets here or in my github repositories at janjanjan2018.

Content

Blood gene expressions of microarray samples.

Acknowledgements

NCBI and the GEO grant funded data repositories of gene expression data.

Inspiration

Sick people.

This dataset contains microarray-based gene expression profiles of granulosa cells collected from women diagnosed with Polycystic Ovary Syndrome (PCOS) and from healthy controls. It originates from the NCBI GEO DataSet GDS4399, which was generated to study the molecular mechanisms underlying PCOS pathogenesis and its relationship to insulin resistance, steroidogenesis, and oocyte maturation.

The data were collected using the Affymetrix Human Genome U133 Plus 2.0 Array (GPL570 platform). Each sample corresponds to an RNA expression profile of granulosa cells isolated from ovarian aspirates of PCOS and non-PCOS women undergoing in-vitro fertilization (IVF).

Key Details

NCBI GEO Accession: GDS4399

Source: Gene Expression Omnibus (GEO), NCBI. GEO Accession: GDS4399 Title: Polycystic ovary syndrome: granulosa cells Platform: Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) Authors: Wood JR, et al. (Original study contributors) National Center for Biotechnology Information, U.S. National Library of Medicine. Available at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GDS4399

Recommended citation style (IEEE): [1] J. R. Wood et al., “Polycystic ovary syndrome: granulosa cells,” Gene Expression Omnibus (GEO), GDS4399, NCBI, Bethesda, MD, USA. [Online]. Available: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GDS4399

License: This dataset is part of the public NCBI GEO database and is distributed under the Public Domain / CC0 License for research and educational use. Please cite the original GEO entry when reusing this dataset.

Multiple myeloma (MM) is a common hematological malignancy with poorly understood recurrence and relapse mechanisms. Notably, bortezomib resistance leading to relapse makes MM treatment significantly challenging. To clarify the drug resistance mechanism, we employed a quantitative proteomics approach to identify differentially expressed protein candidates implicated in bortezomib-resistant recurrent and relapsed MM (RRMM). Bone marrow aspirates from five patients newly diagnosed with MM (NDMM) were compared with those from five patients diagnosed with bortezomib-resistant RRMM using tandem mass tag-mass spectrometry (TMT-MS). Subcellular localization and functional classification of the differentially expressed proteins were determined by gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and hierarchical clustering analyses. The top candidates identified were validated with parallel reaction monitoring (PRM) analysis using tissue samples from 11 NDMM and 8 RRMM patients, followed by comparison with the NCBI Gene Expression Omnibus (GEO) dataset of 10 MM patients and 10 healthy controls (accession no.: GSE80608). Thirty-four differentially expressed proteins in RRMM, including proteinase inhibitor 9 (SERPINB9), were identified by TMT-MS. Subsequent functional enrichment analyses of the identified protein candidates indicated their involvement in regulating cellular metabolism, apoptosis, programmed cell death, lymphocyte-mediated immunity, and defense response pathways in RRMM. The top protein candidate SERPINB9 was confirmed by PRM analysis and western blotting as well as by comparison with an NCBI GEO dataset. We elucidated the proteome landscape of bortezomib-resistant RRMM and identified SERPINB9 as a promising novel therapeutic target. Our results provide a resource for future studies on the mechanism of RRMM.

Additional file 2: Table S1. The microarray samples downloaded from NCBI GEO as the raw data to be used as the reference gene expression profiles, each being annotated with its source NCBI GEO sample accession numbers (GSM). Table S2. The reference gene expression signature (RefGES) matrix created in this study. Table S3. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 30% expression levels from breast tissues and 70% from immune cells. Table S4. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 50% expression levels from breast tissues and 50% from immune cells. Table S5. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the simulated bulk tissues with 70% expression levels from breast tissues and 30% from immune cells. Table S6. The mapping of the cell types of NCBI GEO GSE65133 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S7. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 20 human PBMC samples of NCBI GEO GSE65133. Table S8. The mapping of the cell types of NCBI GEO GSE106898 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S9. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 12 human PBMC samples of NCBI GEO GSE106898. Table S10. The mapping of the cell types of NCBI GEO GSE107990 to those of LM22 (CIBERSORT) and the RefGES used in this study. Table S11. The cumulative percentages of observations for the difference between predictions and real values in the benchmark using the 164 human PBMC samples of NCBI GEO GSE107990.

NCBI accession numbers and related metadata from a study of transcriptomic response of Emiliania huxleyi to 2-heptyl-4-quinolone (HHQ). Sequences from this study are available at the NCBI GEO under accession series GSE131846 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE131846

This dataset collected from NCBI - GEO datasets: - GSE144113 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144113) - GSE76200 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76200) - GSE12791 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12791)

These datasets include four paclitaxel-resistant cell lines which includes BAS, HS578T, MCF7 and MDA-MB-231.

Gene expression analysis was performed using R in each of the datasets, which was between control cells and drug-resistant cells. And using different Bioinformatics databases, they were converted into gene symbols. Genes with a p-value of less than 0.05 were also removed.

Background

RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.

Results

With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.

Conclusion

RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.

Methods Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt: mean values calculated from raw reads of replicates, downloaded from gene expression omnibus (dataset GSE143430 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143430).

Haering_etal_extendedDatatable_1a_Tabulamurissenis_3vs12m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1b_Tabulamurissenis_3vs27m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1c_Tabulamurissenis_12vs27m_DEA.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1d_Tabulamurissenis_3vs12m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1e_Tabulamurissenis_3vs27m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_1f_Tabulamurissenis_12vs27m_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2a_Tabulamurissenis_cluster1_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2b_Tabulamurissenis_cluster2_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2c_Tabulamurissenis_cluster3_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2d_Tabulamurissenis_cluster4_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_2e_Tabulamurissenis_cluster5_gpofiler.txt: Tabula muris senis limb muscle data (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132040) from 3, 12 and 27month males, processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3a_DmLeg_cluster1_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3b_DmLeg_cluster2_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3c_DmLeg_cluster3_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3d_DmLeg_cluster4_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3e_DmLeg_cluster5_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3f_DmLeg_cluster6_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3g_DmLeg_cluster7_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3h_DmLeg_cluster8_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3i_DmLeg_cluster9_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3j_DmLeg_cluster10_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3k_DmLeg_cluster11_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Haering_etal_extendedDatatable_3l_DmLeg_cluster12_gpofiler.txt: Haering_etal_extendedData_DmdevLeg_GSE143430_mean.txt processed with RNfuzzyApp (https://gitlab.com/habermann_lab/rna-seq-analysis-app)

Secretory factors in animals play an important role in communication between different cells, tissues and organs. Especially, the secretory factors with specific expression in one tissue may reflect important functions and unique status of that tissue in an organism. In this study, we identified potential tissue-specific secretory factors in the fat, muscle, heart, lung, kidney and liver in the mouse by analyzing microarray data from NCBI’s Gene Expression Omnibus (GEO) public repository and searching and predicting their subcellular location in GeneCards and WoLF PSORT, and then confirmed tissue-specific expression of the genes using semi-quantitative PCR reactions. With this approach, we confirmed 11 lung, 7 liver, 2 heart, 1 heart and muscle, 7 kidney and 2 adipose and liver-specific secretory factors. Among these genes, 1 lung-specific gene - CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), 3 kidney-specific genes - SERPINA1F (serpin peptidase inhibitor, Clade A, member 1F), WFDC15B (WAP four-disulfide core domain 15B) and DEFB29 (defensin beta 29) and 1 liver-specific gene - MUP19 (major urinary protein 19) have not been reported as secretory factors. These genes were tagged with hemagglutinin at the 3’end and then transiently transfected to HEK293 cells. Through protein detection in cell lysate and media using Western blotting, we verified secretion of the 5 genes and predicted the potential pathways in which they may participate in the specific tissue through data analysis of GEO profiles. In addition, alternative splicing was detected in transcripts of CTLA2A and SERPINA1F and the corresponding proteins were found not to be secreted in cell culture media. Identification of novel secretory factors through the current study provides a new platform to explore novel secretory factors and a general direction for further study of these genes in the future.

The best-performing pipeline for each modelling strategy is highlighted in bold.