职位描述
develop novel algorithms and leverage all available DNA and protein sequence bioinformatics, immunopeptidomics, artificial intelligence tools and algorithms to identify key personalized neoantigens.
1.Utilize state-of-the-art sequence-data mining technologies to process and analyze large sequences dataset of whole-exome sequencing (WES) and RNA sequencing (RNA-seq), to identify somatic mutations, RNA expression, and human leukocyte antigen (HLA) alleles.
2.Use available and emerging state-of-the-art tools and AI-driven algorithms to identify potential neoantigen peptides, to predict HLA-I binding affinity, and to prioritize the peptides.
3.Developing and implementing a set of best practices to predict high-quality immunogenic neoantigens for better personalized neoantigen cancer vaccine.
4.Develop and apply novel combinatorial optimization algorithms to solve the needs and challenges specific to neoantigen prediction, continue formalizing, streamlining, and improving the neoantigen identification process.
1.PhD in Bioinformatics, Computational Biology, with 1+ years of related experience, or MS in Bioinformatics with 4+ years of related experience.
2.Proficient in next generation sequencing (WES sequencing and RNASeq) data analysis, including data quality assessment, sequencing data assembling, mapping, mutation and structural variation analysis, transcript profiling, HLA typing, and splicing analysis.
3.Familiar with commonly used variant-calling software, such as Deep Variant, GATK MuTect2, and VarScan2, etc
4.Experience in prediction of neoantigen peptides, familiar with common tools such as NetChop, Proteasome Cleavage Prediction Server (PCPS), and NetCTL, etc
5.Experience in modeling and predicting peptide-HLA-I binding, familiar with tools and algorithms such as NetMHCpan4.1, MHCflurry2.0, ConvMHC, and PAComplex, etc
6.Expertise in tools and algorithms for MHC-I neoantigen prioritization models,such as pVAC-Seq, TSNAD, CloudNeo, TIminer, MuPeXI, INTEGRATE-Neo, NeoantigenR, HLAthena, EDGE, and DeepHLApan.
7.Expertise in tools for the prediction of T cells epitopes, such as motif-based system, matrix, SVM, empirical scoring, Machine learning algorithms (MLAs), and molecular dynamics (MDs) methods.
8.Familiar with publicly available immunopeptidome databases, such as IEDB and CEDAR, etc
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百奥泰是一家位于中国广州,基于科学而创新的全球性生物制药企业。公司致力于开发新一代创新药和生物类似药,用于治疗肿瘤、自身免疫性疾病、心血管疾病、眼科以及其它危及人类生命或健康的疾病。作为新一代抗体药物研发的领导者,百奥泰已推动多款候选药物进入后期临床试验,其中格乐立®(阿达木单抗)、普贝希®(贝伐珠单抗)、施瑞立®(托珠单抗)已在中国获批上市。公司现有25款在研产品处于不同临床研究阶段, 其中肿瘤领域主要聚焦后PD-1时代的肿瘤免疫治疗和抗体药物偶联体(ADC)靶向药物开发。百奥泰始终以患者的福祉作为首要核心价值,通过创新研发,为患者提供安全、有效、可负担的优质药物,以满足亟待解决的治疗需求。欲了解更多信息,请访问www.bio-thera.com,或关注我们的推特(@bio_thera_sol)和微信公众号(百奥泰)。