cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dhwc79
title: Occurrence Download

description: A dataset containing 38446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.6.4.2_1",
 "TaxonKey is Chordata",
 "Year 2010-2024"
 ]
}

 The dataset includes 38446 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027981-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25504/fairsharing.fe0e02
title: FAIRsharing record for: Pesticide Properties DataBase
title: PPDB

description: This FAIRsharing record describes:  The PPDB is a comprehensive relational database of pesticide chemical identity, physicochemical, human health and ecotoxicological data. It has been developed by the Agriculture & Environment Research Unit (AERU) at the University of Hertfordshire for a variety of end users to support risk assessments and risk management.
description: The source of this description is the metadata record on FAIRsharing.org, an educational and informative resource that describes and links databases, standards, and data policies. FAIRsharing also creates collections of these resources and recommendations of databases and standards based on 3rd party data policies.
DOI: 10.25504/fairsharing.00be0d
title: FAIRsharing record for: Veterinary Substances Database
title: VSDB

description: This FAIRsharing record describes:  The VSDB is a comprehensive relational database of physicochemical and toxicological data for veterinary substances.
description: The source of this description is the metadata record on FAIRsharing.org, an educational and informative resource that describes and links databases, standards, and data policies. FAIRsharing also creates collections of these resources and recommendations of databases and standards based on 3rd party data policies.
DOI: 10.5281/zenodo.14292147
title: Neoantigen-6-HLA-Binding-Prediction

description: Neoantigen prediction for HLA binding is a critical component of personalized cancer immunotherapy. Neoantigens, which are tumor-specific antigens resulting from mutations in cancer cells, can be recognized by the immune system, making them promising targets for tailored immunotherapies. Human leukocyte antigen (HLA) molecules play a key role in presenting these neoantigens on the surface of cells, where they can be detected by T-cells, triggering an immune response. This tutorial focuses on predicting HLA binding affinities for potential neoantigens, an essential step in identifying effective targets for immunotherapy. The workflow includes the use of tools such as OptiType and seq2HLA to determine HLA genotypes, as well as data reformatting and querying methods to manage complex data outputs.
DOI: 10.5281/zenodo.14292115
title: Neoantigen-3-FragPipe-Discovery

description: In this tutorial, we will guide you through a bioinformatics workflow aimed at merging neoantigen databases with known human protein sequences, preparing the data for proteomics analysis using FragPipe. This workflow involves processing FASTA files, filtering for unique sequences, and validating the FASTA databases before using FragPipe to perform peptide identification and validation of neoantigens. Throughout the tutorial, you will learn how to integrate multiple datasets, ensuring that you can perform analyses such as the identification of potential neoantigens, which are critical for cancer immunotherapy and vaccine development. The tools and steps covered here are important for any bioinformatics pipeline dealing with proteomics and neoantigen discovery.
DOI: 10.5281/zenodo.14201893
title: Neoantigen-1-Fusion-Database-Generation

description: A neoantigen is a novel peptide (protein fragment) that is produced by cancer cells due to mutations, including gene fusions, that alter the DNA sequence in a way that generates unique proteins not found in normal cells. Because these mutated proteins are unique to the tumor, they are recognized as "foreign" by the immune system. Neoantigens are valuable in immunotherapy because they can serve as specific targets for the immune system, allowing treatments to selectively attack cancer cells while sparing normal tissue. By stimulating an immune response specifically against these neoantigens, therapies like cancer vaccines or T-cell-based treatments can be developed to enhance the body’s natural defense mechanisms, making neoantigens a promising avenue for personalized cancer treatment.

Creating a fusion database is essential in cancer genomics and personalized medicine, as it enables the identification of crucial biomarkers, enhances diagnostic accuracy, and supports therapeutic development. Gene fusions, where parts of two previously separate genes merge, can produce abnormal proteins that drive cancer. Cataloging these fusion events in a database helps researchers identify specific biomarkers linked to cancer types and design more targeted treatments. Additionally, fusion events may lead to unique peptide sequences, known as neoantigens, which are found only in cancer cells. These neoantigens can be targeted by the immune system, making fusion databases valuable in designing personalized immunotherapies like cancer vaccines or T-cell therapies. Some gene fusions also create oncogenic proteins that promote tumor growth, such as the BCR-ABL fusion in chronic myeloid leukemia. Including such information in a database aids in identifying potential therapeutic targets and predicting treatment efficacy. On the diagnostic side, known gene fusions serve as reliable markers, helping clinicians better classify cancer types and choose the most effective treatments. Finally, fusion databases provide a critical reference for researchers studying fusion mechanisms, their impact on disease progression, and their prevalence across cancers, ultimately fueling the discovery of novel treatments and therapies.
DOI: 10.5281/zenodo.14292134
title: Neoantigen-5-PepPointer-Annotation

description: Neoantigens are tumor-specific antigens that arise from somatic mutations in cancer cells. These mutations result in the generation of abnormal peptides that can be presented by the immune system’s Major Histocompatibility Complex (MHC) molecules. Neoantigens are gaining significant attention in cancer immunotherapy, as they hold the potential to be used in personalized vaccines and therapies aimed at stimulating the immune system to target and destroy tumor cells. The process of identifying neoantigens begins with the analysis of genomic data to detect somatic mutations, followed by the prediction of the corresponding peptide sequences that can bind to MHC molecules. This process involves a combination of bioinformatics tools and techniques, including variant calling, peptide prediction, and MHC binding affinity analysis. Successful identification of neoantigens can pave the way for personalized treatment strategies that improve patient outcomes in cancer therapy. This tutorial focuses on the Neoantigen Annotation pipeline, which is designed to assist researchers and clinicians in annotating and predicting neoantigens from high-throughput genomic data. By the end of this tutorial, you will be equipped with the skills to extract meaningful insights from genomic datasets, predict immunogenic peptides, and understand how these peptides interact with the immune system. These insights are essential for advancing the field of personalized immunotherapy and improving cancer treatment outcomes.
DOI: 10.5281/zenodo.14291866
title: Neoantigen-2-Non-Normal-Database-Generation

description: Proteogenomics leverages mass spectrometry (MS)-based proteomics data alongside genomics and transcriptomics data to identify neoantigens—unique peptide sequences arising from tumor-specific mutations. In the initial section of this tutorial, we will construct a customized protein database (FASTA) using RNA-sequencing files (FASTQ) derived from tumor samples. Following this, we will conduct sequence database searches using the resultant FASTA file and MS data to identify peptides corresponding to novel proteoforms, specifically focusing on potential neoantigens. We will then assign genomic coordinates and annotations to these identified peptides and visualize the data, assessing both spectral quality and genomic localization. In this framework, proteogenomics incorporates RNA-Seq data to generate tailored protein sequence databases, enabling the identification of protein sequence variants, including neoantigens, through mass spectrometry analysis.
DOI: 10.5281/zenodo.14292123
title: Neoantigen-4-PepQuery2-Verification

description: Verification is an essential step in the process of analyzing neoantigens to ensure that the identified peptides or proteins are accurate and reliable. Neoantigens, which are novel antigens arising from tumor-specific mutations, are crucial for cancer immunotherapy. However, the prediction and identification of these neoantigens must be rigorously validated to ensure they are truly present and capable of eliciting an immune response. Without proper verification, the therapeutic potential of neoantigens cannot be realized, leading to wasted resources and failed treatments. In this tutorial, we will guide you through a series of bioinformatics tools used to verify novel peptides and proteins predicted from mass spectrometry data. These tools help confirm the presence and specificity of the identified neoantigens by comparing them to protein databases, checking for validation against spectral data, and performing sequence analysis.
DOI: 10.25504/fairsharing.338659
title: FAIRsharing record for: Bio-Pesticides DataBase
title: BPDB

description: This FAIRsharing record describes:  The BioPesticide DataBase (BPDB) is a comprehensive relational database of data relating to pesticides derived from natural substances.
description: The source of this description is the metadata record on FAIRsharing.org, an educational and informative resource that describes and links databases, standards, and data policies. FAIRsharing also creates collections of these resources and recommendations of databases and standards based on 3rd party data policies.
DOI: 10.15468/dl.c5k7fu
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Juncus emmanuelis A.Fern. & García"
 ]
}

 The dataset includes 22 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027986-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6u2zz
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Clinopodium nepeta subsp. subisodontum (Borbás) Govaerts, Calamintha subisodonta Borbás, Calamintha officinalis subsp. subisodonta (Borbás) U.Schneid., Calamintha officinalis subsp. subisidorata (Borbás) U.Schneid., Calamintha ×subisidorata Borbás, Melissa subnuda Waldst. & Kit., Thymus brauneanus Hoppe, Calamintha einseleana F.W.Schultz, Thymus brauneanus Hoppe ex Rchb., Calamintha officinalis subsp. subisodonta (Borbás) U.Schneid. ex Soó, Satureja subisodonta (Borbás) Soó, 1961, Satureja subisodonta (Borbás) Greuter & Burdet, Calamintha officinalis subsp. subisidorata (Borbás) U.Schneid. ex Soó, Clinopodium nepeta nothosubsp. subisidoratum (Borbás) Govaerts, 1999, Clinopodium subnudum (Waldst. & Kit.) Kuntze, Calamintha nepetoides var. subnuda (Waldst. & Kit.) Posp., Satureja calamintha subsp. subnuda (Waldst. & Kit.) Gams, Calamintha subnuda (Waldst. & Kit.) Host, Satureja subnuda (Waldst. & Kit.) Dörfl., Thymus subnudus (Waldst. & Kit.) Spreng., Calamintha officinalis subsp. subnuda (Waldst. & Kit.) Soó, Clinopodium einseleanum (F.W.Schultz) Peruzzi & F.Conti, Satureja einseleana (F.W.Schultz) Hayek, Calamintha brauneana (Hoppe ex Rchb.) O.Schwarz, Satureja brauneana (Hoppe ex Rchb.) Jáv.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 35 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027987-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qj7hzn
title: Occurrence Download

description: A dataset containing 131 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Salvia subrotunda A.St.-Hil. ex Benth."
 ]
}

 The dataset includes 131 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027985-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25504/fairsharing.eb2188
title: FAIRsharing record for: The National Ecological Observatory Network 
title: NEON Data Portal

description: This FAIRsharing record describes:  The National Science Foundation's National Ecological Observatory Network (NEON) is a continental-scale observation facility research data infrastructure, operated by Battelle, and designed to collect long-term open access ecological data. The NEON data portal provides access to standardized and quality-controlled data product types including meteorological, soil, organismal, biogeochemical, freshwater aquatic, and remote sensing data across application domains. 
description: The source of this description is the metadata record on FAIRsharing.org, an educational and informative resource that describes and links databases, standards, and data policies. FAIRsharing also creates collections of these resources and recommendations of databases and standards based on 3rd party data policies.
DOI: 10.48579/pro/tdh6f4/3hzuqw
title: targeted_exons.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/be1zvm
title: Baits_sequences_batch2.fas

description: :unav
DOI: 10.48579/pro/tdh6f4/gxg1jz
title: Baits_sequences_batch1.fas

description: :unav
DOI: 10.48579/pro/tdh6f4/kzxram
title: Batch1_Conus_Scaffold_19_41441589-41441776.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/ttjnep
title: Batch1_Conus_Scaffold_32_52747163-52747343.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/5beisl
title: Batch1_Conus_Scaffold_5_73677554-73677725.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/cbrsmu
title: Batch1_Conus_Scaffold_25_20923126-20923372.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/d8lmfm
title: Batch1_Conus_Scaffold_8_19082280-19082455.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/iggz3z
title: Batch1_Conus_Scaffold_26_54774937-54775118.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/rtcx4q
title: Batch1_Conus_Scaffold_7_6866840-6867164.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/xzjqv4
title: Batch1_Conus_Scaffold_10_56685552-56685768.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.5281/zenodo.14292295
title: Neoantigen-7-HLA-Binding-Novel-Peptides

description: Neoantigens are peptides derived from tumor-specific mutations, which are recognized by the immune system as foreign and can stimulate an immune response against cancer cells. Identifying these neoantigens is a crucial step in the development of personalized cancer immunotherapies, as they serve as targets for T-cell mediated immune responses. However, predicting which peptides from the tumor genome will bind effectively to major histocompatibility complex (MHC) molecules—key proteins that present antigens to immune cells—remains a significant challenge. This tutorial outlines a comprehensive workflow for the identification, prediction, and validation of potential neoantigens. We begin by using the Immune Epitope Database (IEDB) to predict the binding affinity of peptide sequences to MHC molecules. IEDB provides powerful tools to model how peptides interact with different MHC alleles, helping to prioritize peptides that are most likely to be presented by the immune system. Next, we validate these peptides using PepQuery, a tool that allows for the comparison of predicted neoantigens with experimental proteomics data, providing an additional layer of confidence in their relevance. Finally, we categorize the peptides into strong and weak binders, based on their predicted affinity, which helps in identifying the most promising candidates for cancer immunotherapy.
DOI: 10.48579/pro/tdh6f4/7ngpda
title: Batch1_Conus_Scaffold_16_30431680-30431865.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/adfnzd
title: Batch1_Conus_Scaffold_3_51606748-51606952.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/l3qeiy
title: Batch1_Conus_Scaffold_18_21149259-21149441.exonfasta.aligned_CORR.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/osblt5
title: Batch1_Conus_Scaffold_19_7401033-7401291.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/r0xdsc
title: Batch1_Conus_Scaffold_6_101382979-101383241.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/tgts6n
title: Batch1_Conus_Scaffold_14_45526209-45526396.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/axropf
title: Batch1_Conus_Scaffold_15_84464199-84464361.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/gwwu7h
title: Batch1_Conus_Scaffold_27_32914717-32914910.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/jaqhea
title: Batch1_Conus_Scaffold_10_13263374-13263548.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/oi6fia
title: Batch1_Conus_Scaffold_23_53899697-53899864.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/oixikg
title: Batch1_Conus_Scaffold_3_79942076-79942295.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/bpirto
title: Batch1_Conus_Scaffold_2_138239768-138239961.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/oulvys
title: Batch1_Conus_Scaffold_6_59150579-59150741.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/tlrhu4
title: Batch1_Conus_Scaffold_12_21796245-21796508.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/vgjln4
title: Batch1_Conus_Scaffold_28_32159213-32159409.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/jmtrho
title: Batch1_Conus_Scaffold_19_32627551-32627714.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/k0b8zd
title: Batch1_Conus_Scaffold_4_109551837-109552356.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/nzwo3t
title: Batch1_Conus_Scaffold_21_18685412-18685660.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/qdjbv0
title: Batch1_Conus_Scaffold_1_137976654-137976871.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/sc2qel
title: Batch1_Conus_Scaffold_23_1375108-1375291.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/z40opl
title: Batch1_Conus_Scaffold_1_110967018-110967202.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/2bf5hd
title: Batch1_Conus_Scaffold_27_19070700-19070903.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/hpsgae
title: Batch1_Conus_Scaffold_16_29212268-29212440.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/kiw7pb
title: Batch1_Conus_Scaffold_21_71506712-71507074.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/mglgbx
title: Batch1_Conus_Scaffold_27_670777-670952.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/t3xipk
title: Batch1_Conus_Scaffold_16_85571809-85572005.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/tderuh
title: Batch1_Conus_Scaffold_2_27415090-27415270.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/5x3pwj
title: Batch1_Conus_Scaffold_3_54262187-54262407.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/iu2tkk
title: Batch1_Conus_Scaffold_11_52807137-52807341.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/njeimo
title: Batch1_Conus_Scaffold_18_5667732-5667928.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/tbfawv
title: Batch1_Conus_Scaffold_6_101292969-101293134.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/uelnrg
title: Batch1_Conus_Scaffold_11_5400218-5400425.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/0ef84n
title: Batch1_Conus_Scaffold_4_63341475-63341742.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/1ylhjk
title: Batch1_Conus_Scaffold_4_32950147-32950345.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/c3oowr
title: Batch1_Conus_Scaffold_1_134684350-134684554.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/nskai1
title: Batch1_Conus_Scaffold_3_79942971-79943178.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/8yyoew
title: Batch1_Conus_Scaffold_1_134675037-134675230.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/j1mrfq
title: Batch1_Conus_Scaffold_8_76276672-76276851.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/v4nwvc
title: Batch1_Conus_Scaffold_28_27682610-27682891.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/vc3c98
title: Batch1_Conus_Scaffold_3_143525337-143525550.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/y9raxs
title: Batch1_Conus_Scaffold_11_2202714-2202883.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/1cbqh8
title: Batch1_Conus_Scaffold_6_74863090-74863266.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/dj9nff
title: Batch1_Conus_Scaffold_11_26721669-26721932.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/furou7
title: Batch1_Conus_Scaffold_18_64305061-64305309.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/vdmsjj
title: Batch1_Conus_Scaffold_1_143713391-143713558.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/xzafrf
title: Batch1_Conus_Scaffold_27_19071884-19072084.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/zv9s8v
title: Batch1_Conus_Scaffold_24_62680171-62680398.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav
DOI: 10.48579/pro/tdh6f4/1if1hj
title: Batch1_Conus_Scaffold_8_14799554-14799850.exonfasta.aligned.nuc123.renamed.replicates.fasta

description: :unav