Synthetic Gene Assembly Cloud API Market Outlook

According to our latest research, the global Synthetic Gene Assembly Cloud API market size was valued at USD 1.12 billion in 2024. The market is growing at a robust CAGR of 17.6% and is expected to reach USD 5.44 billion by 2033. This rapid expansion is driven by the increasing demand for high-throughput gene synthesis solutions across the pharmaceutical, biotechnology, and academic sectors, fueled by advancements in cloud computing and API integration. The convergence of synthetic biology and digital platforms is reshaping the landscape, making gene assembly processes more accessible, scalable, and efficient than ever before.

One of the primary growth factors propelling the Synthetic Gene Assembly Cloud API market is the unprecedented surge in synthetic biology research and its applications in drug discovery, therapeutics, and agricultural biotechnology. As pharmaceutical and biotechnology companies race to accelerate R&D timelines, the need for streamlined, automated, and scalable gene synthesis solutions has never been greater. Cloud-based APIs offer seamless integration with laboratory information management systems (LIMS), providing researchers with real-time access to gene assembly tools, data analytics, and quality control features. This digital transformation is reducing manual errors, enhancing reproducibility, and driving down operational costs, thereby attracting significant investments from both public and private sectors.

Another key driver is the growing adoption of personalized medicine and precision agriculture, which require custom gene constructs for tailored therapies and genetically modified crops. Synthetic gene assembly cloud APIs empower scientists to design, assemble, and test novel genetic sequences with unprecedented speed and accuracy. The flexibility of these APIs allows organizations to rapidly iterate genetic designs, optimize gene expression, and adapt to evolving scientific needs. Furthermore, the integration of artificial intelligence and machine learning algorithms into cloud platforms is enhancing the predictive capabilities of gene synthesis, enabling more efficient screening and selection of optimal gene variants. This technological synergy is creating new opportunities for innovation across multiple industries.

The increasing collaboration between academic research institutes, contract research organizations (CROs), and commercial entities is further accelerating market growth. Academic institutions are leveraging cloud-based gene assembly APIs to democratize access to advanced synthetic biology tools, fostering interdisciplinary research and education. CROs, on the other hand, are utilizing these platforms to offer value-added services such as gene optimization, functional validation, and regulatory compliance support to their clients. This ecosystem approach is not only expanding the customer base for Synthetic Gene Assembly Cloud API providers but also driving the standardization of protocols and data formats, which is critical for large-scale adoption.

Regionally, North America continues to dominate the market due to its well-established biotechnology sector, robust funding environment, and early adoption of cloud technologies. However, Asia Pacific is emerging as the fastest-growing region, driven by significant investments in life sciences infrastructure, favorable government policies, and a burgeoning startup ecosystem. Europe also holds a substantial market share, supported by strong academic research networks and increasing public-private partnerships. Latin America and the Middle East & Africa are gradually catching up, with growing awareness and investment in synthetic biology research. The global nature of the Synthetic Gene Assembly Cloud API market ensures that innovation and growth are not confined to a single geography, but are being shaped by collaborative efforts worldwide.

Component Analysis

The Synthetic Gene Assembly Cloud API market is segmented by component into sof

Honey bees are vital pollinators in agriculture and important model insects. To understand the genetic and molecular aspects in their development, a reverse transcription quantitative polymerase chain (RT-qPCR) is used to investigate the target genes. However, it is essential to use the appropriate reference genes as endogenous controls for accurate normalization of target genes. To identify stable reference genes in two honey bee species, [Apis mellifera (Am) and Apis cerana (Ac)], we evaluated eight candidate reference genes including, actin, atub, ef1α, gapdh, rpl13a, rpl32, rps18 and tif. Worker bees belonging to the two species were collected at each developmental day during the embryonic and postembryonic developmental stages. The tyrosine hydroxylase (th) gene was used as the target gene to validate the selected reference genes. Our results revealed that rpl13a was the most stable reference gene at all developmental stages of Am and Ac. In addition, gene combinations, including Amrpl13a & Amrps18 & Amactin, Amrpl13a & Amrpl32, Acrpl13a & Acrpl32, Acrpl13a & Acrpl32 & Acef1α followed by other combinations effectively normalized the expression of the target genes during the embryonic and postembryonic developmental stages of Am and Ac, respectively. Our findings provide a foundation for standardized RT-qPCR analysis to improve the accuracy of genes normalization during the different developmental stages of honey bees.

A fully queryable REST API with JSON, XML, and CSV output as well as inline, runable examples using data from the transcriptional profiling and phenotypic characterization of the major human opportunistic fungal pathogen, Candida albicans, grown in spaceflight conditions.

The evigene_honeybee_apismel2014 archive package contains a complete, reconstructed gene set of Apis mellifera, honey bee, produced with EvidentialGene methods. This file set includes gene sequences, annotations, analyses and summary documents.

The variant-specific annotation fields available from MyVariant.info. (XLS 58 kb)

We sought to determine the transcriptional changes occuring in the fat body of 3d and 8d worker bees fed either a control or pollen-restricted diet.

Oligonucleotide API Market Outlook

The global oligonucleotide API market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach USD 6.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 10.6% during the forecast period. This significant growth is driven by the increasing prevalence of chronic diseases, advancements in biotechnology, and the growing adoption of oligonucleotide therapeutics. The rising demand for personalized medicine and targeted therapies is also contributing to the market's expansion.

One of the primary growth factors for the oligonucleotide API market is the escalating incidence of chronic diseases, such as cancer, cardiovascular diseases, and genetic disorders. Oligonucleotide-based therapies have shown considerable promise in treating these conditions due to their ability to specifically target and modulate genetic sequences. This has led to increased investment in oligonucleotide research and development, further propelling market growth. Additionally, the advancement in delivery technologies and the development of novel oligonucleotide formulations have enhanced the efficacy and safety of these therapies, making them more appealing to both healthcare providers and patients.

Advancements in biotechnology and molecular biology have also played a crucial role in the growth of the oligonucleotide API market. The development of sophisticated techniques such as CRISPR-Cas9 for gene editing and RNA interference (RNAi) has opened new avenues for the application of oligonucleotides. These technologies allow for precise and efficient gene modulation, which is essential for developing targeted therapies and personalized medicine. Moreover, the increasing availability of high-throughput sequencing and bioinformatics tools has accelerated the discovery and development of novel oligonucleotide therapeutics, further driving market growth.

The growing demand for personalized medicine is another significant factor contributing to the expansion of the oligonucleotide API market. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, which often involves the use of oligonucleotide-based therapies. These therapies can be designed to target specific genetic mutations or pathways, offering a more effective and personalized approach to treatment. As the healthcare industry continues to shift towards personalized medicine, the demand for oligonucleotide APIs is expected to rise, driving market growth.

Regionally, North America holds the largest share of the oligonucleotide API market, followed by Europe and Asia Pacific. The dominance of North America can be attributed to the presence of a well-established healthcare infrastructure, significant investment in research and development, and the early adoption of advanced technologies. Europe also has a strong presence in the market, driven by government initiatives and funding for biotechnology research. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, owing to the increasing prevalence of chronic diseases, rising healthcare expenditure, and growing investments in biotechnology.

Oligonucleotide Synthesis Services have become a cornerstone in the biotechnology industry, providing essential support for the development of novel therapeutics and diagnostics. These services offer customized solutions for synthesizing oligonucleotides, catering to the specific needs of pharmaceutical companies and research institutions. By leveraging advanced synthesis technologies, these services ensure high-quality and precise oligonucleotide production, which is crucial for applications in gene editing, molecular diagnostics, and therapeutic development. As the demand for personalized medicine and targeted therapies continues to rise, the role of oligonucleotide synthesis services in facilitating innovative research and product development is becoming increasingly vital. This trend is expected to drive further investment and growth in the oligonucleotide API market, as companies seek reliable partners to support their R&D efforts.

Product Type Analysis

Antisense oligonucleotides are a major segment within the oligonucleotide API market, owing to their extensive application in treating genetic disorders. Th

apoptosis inhibitor 5 Enables fibroblast growth factor binding activity. Involved in negative regulation of apoptotic process. Located in nuclear speck. Biomarker of cervical cancer. This gene encodes an apoptosis inhibitory protein whose expression prevents apoptosis after growth factor deprivation. This protein suppresses the transcription factor E2F1-induced apoptosis and also interacts with, and negatively regulates Acinus, a nuclear factor involved in apoptotic DNA fragmentation. Its depletion enhances the cytotoxic action of the chemotherapeutic drugs. Multiple alternatively spliced transcript variants encoding different isoforms have been identified. [provided by RefSeq, Aug 2011]

Historical NCI Genomic Data Commons data (v09-14-2017). Clinical ('phenotype') and gene expression (HTSeq FPKM-UQ).

TCGA-COAD.GDC_phenotype.tsv

dataset: phenotype - Phenotype

cohortGDC TCGA Colon Cancer (COAD) dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.GDC_phenotype.tsv.gz; Full metadata samples570 version11-27-2017 hubhttps://gdc.xenahubs.net type of dataphenotype authorGenomic Data Commons raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-90 raw datahttps://api.gdc.cancer.gov/data/ input data formatROWs (samples) x COLUMNs (identifiers) (i.e. clinicalMatrix) 570 samples X 151 identifiersAll IdentifiersAll Samples

TCGA-COAD.htseq_fpkm-uq.tsv

dataset: gene expression RNAseq - HTSeq - FPKM-UQ

cohortGDC TCGA Colon Cancer (COAD) dataset IDTCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv downloadhttps://gdc.xenahubs.net/download/TCGA-COAD/Xena_Matrices/TCGA-COAD.htseq_fpkm-uq.tsv.gz; Full metadata samples512 version09-14-2017 hubhttps://gdc.xenahubs.net type of datagene expression RNAseq unitlog2(fpkm-uq+1) platformIllumina ID/Gene Mappinghttps://gdc.xenahubs.net/download/probeMaps/gencode.v22.annotation.gene.probeMap.gz; Full metadata authorGenomic Data Commons raw datahttps://docs.gdc.cancer.gov/Data/Release_Notes/Data_Release_Notes/#data-release-80 raw datahttps://api.gdc.cancer.gov/data/ wranglingData from the same sample but from different vials/portions/analytes/aliquotes is averaged; data from different samples is combined into genomicMatrix; all data is then log2(x+1) transformed. input data formatROWs (identifiers) x COLUMNs (samples) (i.e. genomicMatrix) 60,484 identifiers X 512 samples

Supporting data for "The First Highly Contiguous Genome Assembly of Pikeperch (Sander lucioperca), an Emerging Aquaculture Species in Europe"

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Abstract:

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The pikeperch (Sander lucioperca) is a fresh and brackish water Percid fish natively inhabiting the northern hemisphere. This species is emerging as a promising candidate for intensive aquaculture production in Europe. Specific traits like cannibalism, growth rate and meat quality require genomics based understanding, for an optimal husbandry and domestication process. Still, the aquaculture community is lacking an annotated genome sequence to facilitate genome-wide studies on pikeperch. Here, we report the first highly contiguous draft genome assembly S. lucioperca. In total, 413 and 66 giga base pairs of DNA sequencing raw data were generated with Illumina platform and PacBio Sequel System, respectively. The PacBio data were assembled into a final assembly size of ~900 Mb covering 89% of the 1,014 Mb estimated genome size. The draft genome consisted of 1,966 contigs ordered into 1,313 scaffolds. The contig and scaffold N50 lengths are 3.0 Mb and 4.9 Mb, respectively. The identified repetitive structures accounted for 39% of the genome. We utilized homologies to other ray-finned fishes, and ab initio gene prediction methods to predict 21,249 protein-coding genes in the S. lucioperca genome, of which 88% were functionally annotated by either sequence homology or protein domains and signatures search. The assembled genome spans 97.6% and 96.3% of Vertebrate respectively Actinopterygii single-copy orthologs. The outstanding mapping rate (99.9%) of genomic PE-reads on the assembly suggests an accurate and nearly complete genome reconstruction. This draft genome sequence is the first genomic resource for this promising aquaculture species. It will provide an impetus for genomic-based breeding studies targeting phenotypic and performance traits of captive pikeperch.

Files:

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sanlu.cds.renamed.fa - Coding sequences of predicted protein-coding genes

sanlu.genes.filt.gff3 - gff3 file of predicted protein coding genes

sanlu.genes.pep.fa - predicted peptide sequences

sanlu.genome.ctg.fasta - Sander lucioperca genome assembly at contig-level

sanlu.genome.scf.fa - Sander lucioperca genome assembly at scaffold-level

Sanlu.genome.masked.fasta - Repeats-masked Sander lucioperca genome assembly at scaffold-level

Sanlu.genome.repeats.gff - Gff3 file of predicted repeats in Sander lucioperca genome

Additional_File_2.xlsx - Functional annotations of Sander lucioperca genes by SwissProt, NR RefSeq, TrEMBL and InterPro databases

sanlu.repeats.lib.fasta - Predicted repeats library in Sander lucioperca in FASTA format

sanlu_miRNA.csv Predicted micro RNA families in CSV tab file

sanlu_miRNA.bed - Predicted micro RNA families in BED file format

sanlu_miRNA.html - Predicted micro RNA families in HTML

sanlu_rRNA.fasta - Predicted ribosomal RNA (rRNA) sequences in FASTA file format

sanlu_rRNA.gff - Predicted ribosomal RNA (rRNA) sequences in GFF file format

trna.genes.csv - Predicted transfer RNA (tRNA) genes in CSV tab file

SpeciesTree_rooted_node_labels.txt - Predicted phylogenetic tree in NEWICK format

SpeciesTreeAlignment.fa - Species tree alignment in FASTA, based on 1.1 single copy orthologs

The oligonucleotide API market is experiencing robust growth, driven by the increasing demand for oligonucleotide-based therapeutics in various applications. The market's expansion is fueled by advancements in oligonucleotide technology, leading to the development of more effective and targeted therapies for a wider range of diseases. Contract Manufacturing Organizations (CMOs) are playing a crucial role in this growth, providing manufacturing expertise and capacity to pharmaceutical companies developing these complex APIs. Significant market segments include antisense oligonucleotides, siRNA, miRNA, and CpG oligonucleotides, each catering to specific therapeutic areas. The pharmaceutical industry's focus on personalized medicine and gene therapy further contributes to the market's upward trajectory. Competition among key players like Alnylam Pharmaceuticals, Biogen, and Sarepta Therapeutics is driving innovation and ensuring a steady supply of high-quality oligonucleotide APIs. While challenges remain, such as the high cost of development and manufacturing, and regulatory hurdles, the overall market outlook is positive, driven by continued research and development efforts, and the increasing success of oligonucleotide-based therapies in clinical trials. North America currently dominates the oligonucleotide API market, due to the concentration of major pharmaceutical companies and advanced research infrastructure. However, the Asia-Pacific region is projected to witness significant growth over the forecast period, fueled by rising healthcare expenditure and an expanding pharmaceutical industry. The market segmentation reveals that antisense oligonucleotides API currently holds the largest market share among the various types, reflecting its wider adoption in treating various diseases. The continued innovation in oligonucleotide delivery systems and the increasing understanding of their mechanism of action will further propel the market's expansion. The ongoing clinical trials and approvals of novel oligonucleotide-based drugs will contribute significantly to market expansion, leading to higher demand for high-quality and cost-effective APIs. Further growth is also expected from emerging markets, as increasing awareness and accessibility to advanced medical treatments drive adoption.

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Lake Sinai Viruses (LSV) are common ribonucleic acid (RNA) viruses of honey bees (Apis mellifera) that frequently reach high abundance but are not linked to overt disease. LSVs are genetically heterogeneous and collectively widespread, but despite frequent detection in surveys, the ecological and geographic factors structuring their distribution in A. mellifera are not understood. Even less is known about their distribution in other species. Better understanding of LSV prevalence and ecology have been hampered by high sequence diversity within the LSV clade. We developed a new genetic assay that detects all currently known lineages. We also performed pilot metagenetic sequencing to quantify the diversity of LSV sequences. The resulting data are summarized herein.

Information about the dataset files:

1) pancan_rnaseq_freeze.tsv.gz: Publicly available gene expression data for the TCGA Pan-cancer dataset. File: PanCanAtlas EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/3586c0da-64d0-4b74-a449-5ff4d9136611] [https://doi.org/10.1016/j.celrep.2018.03.046]

2) pancan_mutation_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset. File: mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

3) pancan_GISTIC_threshold.tsv.gz: Publicly available Gene- level copy number information of the TCGA Pan-cancer dataset. This file is processed using script process_copynumber.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. The files copy_number_loss_status.tsv.gz and copy_number_gain_status.tsv.gz generated from this data are used as inputs in our Galaxy pipeline. [https://xenabrowser.net/datapages/?cohort=TCGA%20Pan-Cancer%20(PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443] [https://doi.org/10.1016/j.celrep.2018.03.046]

4) mutation_burden_freeze.tsv.gz: Publicly available Mutational information for TCGA Pan-cancer dataset mc3.v0.2.8.PUBLIC.maf.gz was processed using script process_sample_freeze.py by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/][http://api.gdc.cancer.gov/data/1c8cfe5f-e52d-41ba-94da-f15ea1337efc] [https://doi.org/10.1016/j.celrep.2018.03.046]

5) sample_freeze.tsv or sample_freeze_version4_modify.tsv: The file lists the frozen samples as determined by TCGA PanCancer Atlas consortium along with raw RNAseq and mutation data. These were previously determined and included for all downstream analysis All other datasets were processed and subset according to the frozen samples.[https://github.com/greenelab/pancancer/]

6) vogelstein_cancergenes.tsv: compendium of OG and TSG used for the analysis. [https://github.com/greenelab/pancancer/]

7) CCLE_DepMap_18Q1_maf_20180207.txt.gz Publicly available Mutational data for CCLE cell lines from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2FCCLE_DepMap_18Q1_maf_20180207.txt]

8) ccle_rnaseq_genes_rpkm_20180929.gct.gz: Publicly available Expression data for 1019 cell lines (RPKM) from Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://depmap.org/portal/download/api/download/external?file_name=ccle%2Fccle_2019%2FCCLE_RNAseq_genes_rpkm_20180929.gct.gz]

9) CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct: Publicly available merged Mutational and copy number alterations that include gene amplifications and deletions for the CCLE cell lines. This data is represented in the binary format and provided by the Broad Institute Cancer Cell Line Encyclopedia (CCLE) / DepMap Portal. [https://data.broadinstitute.org/ccle_legacy_data/binary_calls_for_copy_number_and_mutation_data/CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct]

10) GDSC_cell_lines_EXP_CCLE_names.csv.gz Publicly available RMA normalized expression data for Genomics of Drug Sensitivity in Cancer(GDSC) cell-lines. File gdsc_cell_line_RMA_proc_basalExp.csv was downloaded. This data was subsetted to 389 cell lines that are common among CCLE and GDSC. All the GDSC cell line names were replaced with CCLE cell line names for further processing. [https://www.cancerrxgene.org/gdsc1000/GDSC1000_WebResources//Data/preprocessed/Cell_line_RMA_proc_basalExp.txt.zip]

11) GDSC_CCLE_common_mut_cnv_binary.csv.gz: A subset of merged Mutational and copy number alterations that include gene amplifications and deletions for common cell lines between GDSC and CCLE. This file is generated using CCLE_MUT_CNA_AMP_DEL_binary_Revealer.gct and a list of common cell lines.

12) gdsc1_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC1 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC1_fitted_dose_response_15Oct19.xlsx]

13) gdsc2_ccle_pharm_fitted_dose_data.txt.gz: Pharmacological data for GDSC2 cell lines. [ftp://ftp.sanger.ac.uk/pub/project/cancerrxgene/releases/current_release/GDSC2_fitted_dose_response_15Oct19.xlsx]

14) compounds.csv: list of pharmacological compounds tested for our analysis

15) tcga_dictonary.tsv: list of cancer types used in the analysis.

16) seg_based_scores.tsv: Measurement of total copy number burden, Percent of genome altered by copy number alterations. This file was used as part of the Pancancer analysis by Gregory Way et al as described in https://github.com/greenelab/pancancer/ data processing and initialization steps. [https://github.com/greenelab/pancancer/]

The Asian honeybee, Apis cerana, is an ecologically and economically important pollinator. Mapping its genetic variation is key to understanding population-level health, histories, and potential capacities to respond to environmental changes. However, most efforts to date were focused on single nucleotide polymorphisms (SNPs) based on a single reference genome, thereby ignoring larger-scale genomic variation. We employed long-read sequencing technologies to generate a chromosome-scale reference genome for the ancestral group of A. cerana. Integrating this with 525 resequencing datasets, we constructed the first pan-genome of A. cerana, encompassing almost the entire gene content. We found that 31.32% of genes in the pan-genome were variably present across populations, providing a broad gene pool for environmental adaptation. We identified and characterized structural variations (SVs) and found that they were not closely linked with SNP distributions, however, the formation of SVs was closely associated with transposable elements. Furthermore, phylogenetic analysis using SVs revealed a novel A. cerana ecological group not recoverable from the SNP data. Performing environmental association analysis identified a total of 44 SVs likely to be associated with environmental adaptation. Verification and analysis of one of these, a 330 bp deletion in the Atpalpha gene, indicated that this SV may promote the cold adaptation of A. cerana by altering gene expression. Taken together, our study demonstrates the feasibility and utility of applying pan-genome approaches to map and explore genetic feature variations of honeybee populations, and in particular to examine the role of SVs in the evolution and environmental adaptation of A. cerana.

The gene-specific annotation fields available from MyGene.info. (XLS 32 kb)

Synergistic effects of multiple stressors underpinned by limited nutrition (Dolezal and Toth, 2018). Poor nutrition is most damaging in developing honey bee larvae, who mature into workers unable to meet the needs of their colony (Scofield and Mattila, 2015). It is therefore essential that we better understand the nutritional landscape experienced by honey bee larvae. In this study, we characterize the metabolic capabilities of a honey bee larvae-associated bacterium, Bombella apis (formerly Parasaccharibacter apium), and its effects on the nutritional resilience of larvae. We found that B. apis is the only bacterium associated with larvae that can withstand the antimicrobial larval diet. Further, we found that B. apis can synthesize all essential amino acids and significantly alters the amino acid content of synthetic larval diet, largely by increasing the essential amino acid lysine. Analyses of gene gain/loss across the phylogeny suggest that four amino acid transporters were gained in recent B. apis ancestors. In addition, the transporter LysE is conserved across all sequenced strains of B. apis. This result suggests that amino acid export is a key feature conserved within the Bombella clade. Finally, we tested the impact of B. apis on developing honey bee larvae subjected to nutritional stress and found that larvae supplemented with B. apis are bolstered against mass reduction despite limited nutrition. Together, these data suggest an important role of B. apis as a nutritional mutualist of honey bee larvae. Methods Supplementary Table 1 – All sequenced B. apis strains retain the ability to synthesize all amino acids. Table generated from conserved core orthologs across the included strains showing presence/absence of amnio acid biosynthesis genes. ‘oid’ refers to the ortholog ID in our analysis of orthologous genes, ‘Name’ refers to the amino acid biosynthesis gene annotation, ‘Pathways/steps/scores’ refers to the biosynthetic pathway in which each gene is found, the enzymatic step in the pathway, and the GapMind score. GapMind score is either 2 (high confidence), 1 (medium confidence), or 0 (low confidence). In the columns below each sequenced strain, ‘1’ means that a given gene was identified in the corresponding genome and ‘0’ means that it was not identified. Supplementary Table 2- All B. apis genomes contain multiple cationic amino acid transporter orthologs. Gene gain/loss analysis showing all the gains and losses across the phylogeny of all sequenced B. apis strains and related microbes in Figure 3. ‘oid’ refers to the ortholog ID in our analysis of orthologous genes, ‘Name’ refers to proteins identified across all genomes analyzed. In the columns below each sequenced strain, ‘g’ means that a given gene was gained by that strain and ‘l’ means that it was lost. Supplementary Table 3 – Accession numbers for all sequenced strains used in this work.

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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

ABSTRACT

It contains the data of drug targets (gene names), uniprot identifiers, secondary linked data sources (e.g., PharmGKB), market drug name, chembl identifier, and pubchem compound identifier obtained from DGIdb.

Instructions:

Data were cleaned and duplicates were removed. Data were all categorical features.

Inspiration:

This dataset uploaded to U-BRITE for "DRG_DEPOT" summer 2023 team project. It is used for constructing R2G dataset, which will map drugs to their drug targets (gene -> protein = drug target)

Acknowledgements

Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, Griffith M, Griffith OL, Wagner AH. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021 Jan 8;49(D1):D1144-D1151. doi: 10.1093/nar/gkaa1084. PMID: 33237278; PMCID: PMC7778926.

https://www.dgidb.org/

U-BRITE last update date: 06/09/2023