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    Additional file 6 of Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes
    Figshare (2023)
    Francisco Avila CobosMohammad Javad Najaf PanahJ ElswoodXiaochen LongTsz‐Kwong ManHua‐Sheng ChiuElad ChomskyEvgeny KinerMichael KruegerDiego di BernardoLuis VolochJan J. MolenaarSander R. van HooffFrank WestermannSelina JanskyMichele S. RedellPieter MestdaghPavel Sumazin
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    Abstract:
    Additional file 6: Table S5. Predicted abundance and clinical annotations of pediatric AML and neuroblastoma TARGET patients that were used to evaluate the outcomes-predictive value of subclone abundances in diagnostic samples.
    Keywords:
    RNA-Seq
    Topics:
    Cancer-related molecular mechanisms research
    Molecular Biology Techniques and Applications
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    Supplementary Table 5 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 5 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
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    Supplementary Table 3 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 3 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    RNA-Seq
    Table (database)
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    Supplementary Table 7 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 7 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    RNA-Seq
    Table (database)
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    Supplementary Table 4 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 4 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    RNA-Seq
    Table (database)
    Citations (0)
    Supplementary Table 4 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 4 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    RNA-Seq
    Table (database)
    Citations (0)
    Supplementary Table 1 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 1 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
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    Table (database)
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    Identification of Salt Stress Response Genes in Rosa chinensis Leaves by Comparative RNA-seq Analysis of Transcriptome Dynamics
    Russian Journal of Plant Physiology (2018)
    Xin‐Xia TianWang ZhQ. ZhangHuaCong CiP. Wang
    RNA-Seq
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    Supplementary Table 10 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
    Jennifer BeaneJessica VickFrank SchembriChristina AnderlindAdam C. Gower
    Supplementary Table 10 from Characterizing the Impact of Smoking and Lung Cancer on the Airway Transcriptome Using RNA-Seq
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    Additional file 2 of RNA-Seq transcriptome reveals different molecular responses during human and mouse oocyte maturation and fertilization
    Figshare (2020)
    Zheng‐Hui ZhaoTie‐Gang MengAng LiHeide SchattenZhen‐Bo Wang
    Additional file 2. Mouse MII oocyte-specific expressed gene list.
    RNA-Seq
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    MOESM1 of BaRTv1.0: an improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq
    Figshare (2019)
    Paulo Rapazote-FloresMicha BayerLinda MilneClaus MayerJohn Fuller
    Additional file 1: Table S1. RNA-seq datasets from both public databases and in house unpublished data. Table S2. StringTie Parameters used in the different assemblies (sorted by transcript number). Table S3. Mean proportions of alternatively spliced products by HR-RT-PCR analyisis. Table S4. Correlation of HR RT-PCR data with BaRTv1.0, BaRTv1.0- QUASI and HORVU transcripts. Table S5. Splice Junctions and intron lengths. Table S6. Differentially expressed gene clusters and differential transcript usage gene clusters. Table S7. Pairwise significant changes in alternatively spliced transcripts detected by HR RT-PCR between different organs.
    RNA-Seq
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