biostudies-arrayexpressRoger Mulet-LazaroHomo sapiensSTARSalmonhttps://www.ebi.ac.uk/biostudies/studies/E-MTAB-15145We generated RNA-seq data of 30 cases of acute leukemia of ambiguous lineage (ALAL), including both bilineal and biphenotypic patients, in order to develop methods to improve the diagnosis of this disease. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data. Second, we generated a machine learning (ML) classifier trained on compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases.biostudies-arrayexpressSample Collection - Samples of acute leukemia of ambiguous lineage (ALAL) patients were collected from the biobank of the Erasmus MC Hematology department (Rotterdam, The Netherlands). Mononuclear cells were isolated from bone marrow or peripheral blood.Sequencing - Amplified sample libraries were paired-end sequenced (2x100 bp) on the Novaseq 6000 platform (Illumina)Library Construction - Sample libraries were prepped using 500 ng of input RNA according to the KAPA RNA HyperPrep Kit with RiboErase (HMR) (Roche) using Unique Dual Index adapters (Integrated DNA Technologies, Inc.).Nucleic Acid Extraction - RNA was isolated using the AllPrep DNA/RNA mini kit (Qiagen, #80204) and converted into cDNA using the SuperScript II Reverse Transcriptase (Thermo Fischer Scientific) according to standard diagnostic procedures.MIAME ScoreOrganizationAssays and DataProcessed DataMAGE-TAB FilesSequence Alignment - Sequencing reads were aligned against the human genome (hg38) using STAR v2.5.4bData Transformation - Salmon was used to quantify expression of individual transcripts, which were subsequently aggregated to estimate gene-level abundances with the R package tximport. Human gene annotation derived from Ensembl v104 was used for the quantification.UnknownTranscriptomicsGenomicsProteomicsIllumina NovaSeq 6000RNA-seq of total RNAHomo sapiensRoger Mulet-LazarofalseRNA-seq data of acute leukemias of ambiguous lineage (ALAL)We generated RNA-seq data of 30 cases of acute leukemia of ambiguous lineage (ALAL), including both bilineal and biphenotypic patients, in order to develop methods to improve the diagnosis of this disease. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data. Second, we generated a machine learning (ML) classifier trained on compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases.2025-05-16T00:00:00Z2025-05-09T16:43:09.275Z2025-05-09T16:43:09.275ZE-MTAB-15145EGAS00001007967EGAD00001015547EFO_0002944EFO_0004170EFO_0009653EFO_0004917EFO_0005518EFO_0003816EFO_0004184