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John Collins

Bioinformatics Data Scientist (Remote), M.Sc. (UCSC) | Python Software Engineer | NGS Pipelines | Custom Web Applications | R&D Databases

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John Collins

University of California, Santa Cruz

Ventura, California, United States Contact info

500+ connections

AboutAbout

Recent Bay Area ex-pat living in Ventura in Southern California. I am currently actively pursuing remote bioinformatics roles. Thank you for looking over my background and considering me. I have six years of experience working as a Bioinformatics Scientist in the R&D departments of four different companies. At the same time, I lived in the San Francisco Bay Area from 2014-2020, after receiving my master's in Biomolecular Engineering and Bioinformatics from the University of California (Santa Cruz) in June of 2014. I am actively pursuing potential new opportunities.

Highest Education:
UCSC Bioinformatics M.Sc., 2014

Industry Genomics Experience (2014–):
First-hand data analysis on hundreds of next-generation high-throughput sequencing runs. Diverse Illumina® expertise spanning secondary through tertiary pipeline analysis for computational interpretation of NGS raw sequence data; with experience in clinical-grade assay development (CareDx’s cell-free DNA NGS test AlloSure™), technical product marketing of (RUO) commercial avant-garde NGS kits (BioRad’s ddSEQ™), as well as proprietary internal pharmaceuticals R&D (BMS’s Biologics Lead Discovery), leveraging some of the latest techniques in NGS such as ultra-deep targeted panels, low-frequency variant calling and genotyping (e.g., sporadic [<1%] mutants), single-cell transcriptomics, unique molecular barcoding, and immunoinformatics (e.g., single-cell B/TCR Repertoires).

Wrote advanced software development proficiency in creating complex custom omics-based analytics using the Python data science stack. 7+ years of continuous growth using SciPy, NumPy, Pandas, Jupyter, Conda (for pkg./virtual env mgmt.), scikit-learn, matplotlib, with finer tailoring of analyses often pulling from the extensive PyPI/Anaconda package repositories. In-depth bash knowledge accumulated from years of working in (containerized) Linux/UNIX environments. Reflexive shell scripting, ssh, (parallel) programming, cloud computing, sequence-based statistics, algorithms, and machine learning (sklearn, theano, keras) skills. Strong fluency in R & HTML/CSS; sufficient working knowledge for integrating/debugging/translating most common languages (e.g., Java, JavaScript[React], C/C++[e.g., Boost, Cython], Perl).

Extensive academic and professional history of analysis integrating bioinformatics tools and databases. Mapping/Alignment: BWA(-MEM), Samtools, STAR, ClustalO[mega]; Annotation: UCSC Genome Browser, Ensembl/ EMBL-EBI/ EMBOSS; NCBI: BLAST, RefSeq, SRA; Cancer Genomics: TCGA; Cloud Computing: AWS / GCP / Azure; SevenBridges; DNAnexus.Recent Bay Area ex-pat living in Ventura in Southern California. I am currently actively pursuing remote bioinformatics roles. Thank you for looking over my background and considering me. I have six years of experience working as a Bioinformatics Scientist in the R&D departments of four different companies. At the same time, I lived in the San Francisco Bay Area from 2014-2020, after receiving my master's in Biomolecular Engineering and Bioinformatics from the University of California (Santa Cruz) in June of 2014. I am actively pursuing potential new opportunities. Highest Education: UCSC Bioinformatics M.Sc., 2014 Industry Genomics Experience (2014–): First-hand data analysis on hundreds of next-generation high-throughput sequencing runs. Diverse Illumina® expertise spanning secondary through tertiary pipeline analysis for computational interpretation of NGS raw sequence data; with experience in clinical-grade assay development (CareDx’s cell-free DNA NGS test AlloSure™), technical product marketing of (RUO) commercial avant-garde NGS kits (BioRad’s ddSEQ™), as well as proprietary internal pharmaceuticals R&D (BMS’s Biologics Lead Discovery), leveraging some of the latest techniques in NGS such as ultra-deep targeted panels, low-frequency variant calling and genotyping (e.g., sporadic [<1%] mutants), single-cell transcriptomics, unique molecular barcoding, and immunoinformatics (e.g., single-cell B/TCR Repertoires). Wrote advanced software development proficiency in creating complex custom omics-based analytics using the Python data science stack. 7+ years of continuous growth using SciPy, NumPy, Pandas, Jupyter, Conda (for pkg./virtual env mgmt.), scikit-learn, matplotlib, with finer tailoring of analyses often pulling from the extensive PyPI/Anaconda package repositories. In-depth bash knowledge accumulated from years of working in (containerized) Linux/UNIX environments. Reflexive shell scripting, ssh, (parallel) programming, cloud computing, sequence-based statistics, algorithms, and machine learning (sklearn, theano, keras) skills. Strong fluency in R & HTML/CSS; sufficient working knowledge for integrating/debugging/translating most common languages (e.g., Java, JavaScript[React], C/C++[e.g., Boost, Cython], Perl). Extensive academic and professional history of analysis integrating bioinformatics tools and databases. Mapping/Alignment: BWA(-MEM), Samtools, STAR, ClustalO[mega]; Annotation: UCSC Genome Browser, Ensembl/ EMBL-EBI/ EMBOSS; NCBI: BLAST, RefSeq, SRA; Cancer Genomics: TCGA; Cloud Computing: AWS / GCP / Azure; SevenBridges; DNAnexus.

FeaturedFeatured

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NextGen Immuno-Oncology (I-O)NextGen Immuno-Oncology (I-O)

pactpharma.compactpharma.com

Personalized Adoptive Cell Therapies with NeoAntigen-Targeted T Cells Against CancerPersonalized Adoptive Cell Therapies with NeoAntigen-Targeted T Cells Against Cancer

LinkLink

The Single Solution for Single-Cell NGSThe Single Solution for Single-Cell NGS

info.bio-rad.cominfo.bio-rad.com

Single-cell RNA-Seq allows researchers to go beyond the bulk averages of traditional RNA-Seq methods to differentiate individual cells in heterogenous populations, providing a higher resolution of cellular differences to facilitate access to cell...Single-cell RNA-Seq allows researchers to go beyond the bulk averages of traditional RNA-Seq methods to differentiate individual cells in heterogenous populations, providing a higher resolution of cellular differences to facilitate access to cell...

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ExperienceExperience

PACT PharmaPACT Pharma
2 yrs 1 mo2 yrs 1 mo

Bioinformatics Data ScientistBioinformatics Data Scientist
Full-timeFull-time Feb 2019 - Sep 2020 · 1 yr 8 mosFeb 2019 - Sep 2020 · 1 yr 8 mos South San Francisco, CASouth San Francisco, CA

Supported the bioinformatic data integration of an incredible interdisciplinary immuno- oncology effort spanning the latest technology in protein sciences, gene editing, and tumor immunology to create a first-of-its-kind patient-tailored cancer-targeting T cell therapy (personalized immunotherapy) applicable to most solid tumor types.

✵ Was the principal database / LIMS manager for all R&D teams, leveraging a cloud platform LIMS/ELN solution called Benchling; including writing custom software using the Benchling API, for cross-R&D team computational / bioinformatic needs such as automated data capture & data structure standardization, clinical sample accessioning, data queries / analysis, etc.

✵ Created a custom sanger sequencing web application (>10,000 lines Python source code) which reduced a multi-hour manual QC and analysis process of gene-edited plasmid sequencing data down to <1 minute fully-automated computation time. Application featured numerous lab scientist end user- requested UI/UX components, and was architected and deployed with 24/7 reliability and scalability allowing for multiple concurrent users on any number of machines/ browsers and parallelized computationally to allow for hundreds of samples to be processed per user session.

"PACT Pharma is dedicated to synthesizing a tsunami of neo-epitope targeted T cells and producing a personalized adoptive cell therapy designed to benefit each individual cancer patient. The neo-epitope targeting is engineered into the patient’s own T cells (autologous T cells) for programming to seek out, infiltrate into the tumor and kill the tumor cells displaying the unique neo-epitopes. In essence, PACT Pharma is engineering next generation synthetic tumor-infiltrating lymphocytes (synthetic TILs), tailored for each patient’s cancer with highly efficient turnaround in manufacturing from tumor biopsy to re-infusion of autologous synthetic TILs back into the patient."

https://pactpharma.com/platform/Supported the bioinformatic data integration of an incredible interdisciplinary immuno- oncology effort spanning the latest technology in protein sciences, gene editing, and tumor immunology to create a first-of-its-kind patient-tailored cancer-targeting T cell therapy (personalized immunotherapy) applicable to most solid tumor types. ✵ Was the principal database / LIMS manager for all R&D teams, leveraging a cloud platform LIMS/ELN solution called Benchling; including writing custom software using the Benchling API, for cross-R&D team computational / bioinformatic needs such as automated data capture & data structure standardization, clinical sample accessioning, data queries / analysis, etc. ✵ Created a custom sanger sequencing web application (>10,000 lines Python source code) which reduced a multi-hour manual QC and analysis process of gene-edited plasmid sequencing data down to <1 minute fully-automated computation time. Application featured numerous lab scientist end user- requested UI/UX components, and was architected and deployed with 24/7 reliability and scalability allowing for multiple concurrent users on any number of machines/ browsers and parallelized computationally to allow for hundreds of samples to be processed per user session. "PACT Pharma is dedicated to synthesizing a tsunami of neo-epitope targeted T cells and producing a personalized adoptive cell therapy designed to benefit each individual cancer patient. The neo-epitope targeting is engineered into the patient’s own T cells (autologous T cells) for programming to seek out, infiltrate into the tumor and kill the tumor cells displaying the unique neo-epitopes. In essence, PACT Pharma is engineering next generation synthetic tumor-infiltrating lymphocytes (synthetic TILs), tailored for each patient’s cancer with highly efficient turnaround in manufacturing from tumor biopsy to re-infusion of autologous synthetic TILs back into the patient." https://pactpharma.com/platform/

Skills: Bioinformatics · Cancer Immunotherapy · NGSSkills: Bioinformatics · Cancer Immunotherapy · NGS

Bioinformatics Data AnalystBioinformatics Data Analyst
Sep 2018 - Feb 2019 · 6 mosSep 2018 - Feb 2019 · 6 mos San Francisco Bay AreaSan Francisco Bay Area

Immunoinformatics for
Personalized Adoptive T-Cell Immuno-Oncology (I-O)

—Neo-Epitope Targeted T Cells—

pactpharma.com

Next generation synthetic tumor-infiltrating lymphocytes (synthetic TILs).

Immunoinformatics for Personalized Adoptive T-Cell Immuno-Oncology (I-O) —Neo-Epitope Targeted T Cells— pactpharma.com Next generation synthetic tumor-infiltrating lymphocytes (synthetic TILs).

Bioinformatics Pipeline DeveloperBioinformatics Pipeline Developer
Bristol-Myers SquibbBristol-Myers Squibb Jan 2018 - Jun 2018 · 6 mosJan 2018 - Jun 2018 · 6 mos Redwood City, CaliforniaRedwood City, California

Scientific computing analysis & custom software development for I-O (immuno-oncology) focused bioinformatics. [Contract Consultant]

Integrated a combination of open source bioinformatics and data science tool resources with custom python programming, shell scripting, and Docker-containerized Linux environment mgmt. for cloud-based, high-performance computing of V(D)J-gene rearrangement genomics analyses (i.e., as enabled from high-throughput [near-exhaustive] NGS investigation of "Antigen Receptor Repertoires"). Many millions of V(D)J gene-recombined antibody sequences are possible from the constituent human germline alleles alone, demanding a depth of molecular survey with immensity excellently suited for recent NGS capabilities.

Such "repertoires" of antibody sequences are now often derived, for example, from murine human-transgenic lymphocytes, phage display screens, synthetic rational designs, and other technologies capable of producing humanized or fully human (i.e., least likely immunogenicity) IgG proteins with clinical potential as immunotherapeutic mfg.'d mAbs.

Scientific computing analysis & custom software development for I-O (immuno-oncology) focused bioinformatics. [Contract Consultant] Integrated a combination of open source bioinformatics and data science tool resources with custom python programming, shell scripting, and Docker-containerized Linux environment mgmt. for cloud-based, high-performance computing of V(D)J-gene rearrangement genomics analyses (i.e., as enabled from high-throughput [near-exhaustive] NGS investigation of "Antigen Receptor Repertoires"). Many millions of V(D)J gene-recombined antibody sequences are possible from the constituent human germline alleles alone, demanding a depth of molecular survey with immensity excellently suited for recent NGS capabilities. Such "repertoires" of antibody sequences are now often derived, for example, from murine human-transgenic lymphocytes, phage display screens, synthetic rational designs, and other technologies capable of producing humanized or fully human (i.e., least likely immunogenicity) IgG proteins with clinical potential as immunotherapeutic mfg.'d mAbs.

Bioinformatician II - Sequencing AnalysisBioinformatician II - Sequencing Analysis
Bio-Rad Laboratories · Full-timeBio-Rad Laboratories · Full-time Apr 2017 - Jul 2017 · 4 mosApr 2017 - Jul 2017 · 4 mos Pleasanton, CaliforniaPleasanton, California

As my primary project, I created a working framework for a secondary and tertiary custom NGS pipeline for a single-cell whole-transcriptome unique-molecular-barcoded RNA-Seq assay leveraging Bio-Rad’s proprietary droplets fluidics technology for the cell isolation and polyA-tail capture, along with Illumina’s Nextera™ technology for library preparation. I developed an advanced cloud computing network on Microsoft Azure, and genomically mapped samples in parallel using a scaling-VM highly-multicore cluster architecture. For tertiary analysis, I used database genome-wide GENCODE annotation (as recommended by STAR) and wrote assay-specific barcode deconvolution algorithms to calculate digital transcript counts and then fed these into various dimensionality reduction functions from Python Scikit-learn, such as k-means clustering, PCA, MDS, and t-SNE. To create high-quality, reproducible & large-scale visualizations I designed an automated workflow for outputting pdf reports using the Python Data Science stack (namely matplotlib, pandas, NumPy, SciPy, scikit-learn, Bokeh, & Seaborn) in a Conda-controlled Azure/Ubuntu-Linux environment.

Then, cloud-side, I SSH port-forward a Jupyter Python3 server for: i) a local powerful browser-interface to the remote VM's, & ii) dual functioning as an archivable/presentable record. This Notebook-UI setup then easily facilitates rapid live reproduction of numerous incrementally varying 2-3D visual perspectives of the samples, revealing more from the combinatorial spectrum of possible parameter tunings of the ML algorithms vs. more biased narrower view (e.g., # of principle components, # of k-means clusters, and # of features/dimensions to select for). Both the simpler linear decomposition method (PCA) and the more complex nonlinear t-Stochastic Neighbor Embedding (t-SNE) manifold-based method yield their own complementary hidden insights into the relationships between individual cells and the
ontology of RNA-defined cell types.As my primary project, I created a working framework for a secondary and tertiary custom NGS pipeline for a single-cell whole-transcriptome unique-molecular-barcoded RNA-Seq assay leveraging Bio-Rad’s proprietary droplets fluidics technology for the cell isolation and polyA-tail capture, along with Illumina’s Nextera™ technology for library preparation. I developed an advanced cloud computing network on Microsoft Azure, and genomically mapped samples in parallel using a scaling-VM highly-multicore cluster architecture. For tertiary analysis, I used database genome-wide GENCODE annotation (as recommended by STAR) and wrote assay-specific barcode deconvolution algorithms to calculate digital transcript counts and then fed these into various dimensionality reduction functions from Python Scikit-learn, such as k-means clustering, PCA, MDS, and t-SNE. To create high-quality, reproducible & large-scale visualizations I designed an automated workflow for outputting pdf reports using the Python Data Science stack (namely matplotlib, pandas, NumPy, SciPy, scikit-learn, Bokeh, & Seaborn) in a Conda-controlled Azure/Ubuntu-Linux environment. Then, cloud-side, I SSH port-forward a Jupyter Python3 server for: i) a local powerful browser-interface to the remote VM's, & ii) dual functioning as an archivable/presentable record. This Notebook-UI setup then easily facilitates rapid live reproduction of numerous incrementally varying 2-3D visual perspectives of the samples, revealing more from the combinatorial spectrum of possible parameter tunings of the ML algorithms vs. more biased narrower view (e.g., # of principle components, # of k-means clusters, and # of features/dimensions to select for). Both the simpler linear decomposition method (PCA) and the more complex nonlinear t-Stochastic Neighbor Embedding (t-SNE) manifold-based method yield their own complementary hidden insights into the relationships between individual cells and the ontology of RNA-defined cell types.

CareDx, Inc.CareDx, Inc.
2 yrs 6 mos2 yrs 6 mos Brisbane, CaliforniaBrisbane, California

Bioinformatics AnalystBioinformatics Analyst
Nov 2016 - Apr 2017 · 6 mosNov 2016 - Apr 2017 · 6 mos

Python-centric R&D software engineering for data analysis & visualization of NGS cell-free DNA results, implementing both standard and custom tools with a focus on assay performance characterization, error correction by integration of various QC-filtering algorithms, and testing of protocol adjustments.

In line with CAP, ACMG, AMP, & ASHG NGS development professional guidelines, orthogonal bioinformatic analysis helps substantiate high confidence in the accuracy and precision of the assay (e.g., exhaustive "truth" confirmation for panel-overlapping SNVs in NIST reference standards ran in-house; in silico artificially mutated amplicons assessing background noise). Without prior genotyping of the highly heterogeneous tetraploid genome mixture that constitutes the circulating cfDNA in Tx patients, complex interpretation of single-nucleotide genotype differences is required. By only considering SNPs known from population studies to have approximately 50% MAF in all humans, binomial modeling assumptions combined with Mendelian principles reveal highly accurate HTS measurement of allele frequency distributions (LOB=0.1%/LOD=0.2%) that directly correlate with varying levels of donor-derived cfDNA (see Python analysis below).

Illumina NGS platforms [ultra-deep targeted sequencing (MiSeq) & WGS/WES/RNA-Seq (HiSeq)] | GNU Linux Unix env.'s, bash shell scripting , SQL databases (PostgreSQL, MySQL, Oracle, NoSQL), NumPy, SciPy, Pandas / R (DataFrame analysis), Jupyter Notebook | Custom data visualization (Matplotlib, Seaborn, Plotly, ggplot2): in-depth changes to default settings for publication-quality figures (e.g., AMP Journal of Molecular Diagnostics, Grskovic et al 2016*) | Translation of custom bioinformatics tools into Django web server / SQL database framework for rapid, click-to-execute generation of data analysis reports available on-demand across teams by automatic detection and upload of new sequencing data.

*[http://dx.doi.org/10.1016/j.jmoldx.2016.07.003]Python-centric R&D software engineering for data analysis & visualization of NGS cell-free DNA results, implementing both standard and custom tools with a focus on assay performance characterization, error correction by integration of various QC-filtering algorithms, and testing of protocol adjustments. In line with CAP, ACMG, AMP, & ASHG NGS development professional guidelines, orthogonal bioinformatic analysis helps substantiate high confidence in the accuracy and precision of the assay (e.g., exhaustive "truth" confirmation for panel-overlapping SNVs in NIST reference standards ran in-house; in silico artificially mutated amplicons assessing background noise). Without prior genotyping of the highly heterogeneous tetraploid genome mixture that constitutes the circulating cfDNA in Tx patients, complex interpretation of single-nucleotide genotype differences is required. By only considering SNPs known from population studies to have approximately 50% MAF in all humans, binomial modeling assumptions combined with Mendelian principles reveal highly accurate HTS measurement of allele frequency distributions (LOB=0.1%/LOD=0.2%) that directly correlate with varying levels of donor-derived cfDNA (see Python analysis below). Illumina NGS platforms [ultra-deep targeted sequencing (MiSeq) & WGS/WES/RNA-Seq (HiSeq)] | GNU Linux Unix env.'s, bash shell scripting , SQL databases (PostgreSQL, MySQL, Oracle, NoSQL), NumPy, SciPy, Pandas / R (DataFrame analysis), Jupyter Notebook | Custom data visualization (Matplotlib, Seaborn, Plotly, ggplot2): in-depth changes to default settings for publication-quality figures (e.g., AMP Journal of Molecular Diagnostics, Grskovic et al 2016*) | Translation of custom bioinformatics tools into Django web server / SQL database framework for rapid, click-to-execute generation of data analysis reports available on-demand across teams by automatic detection and upload of new sequencing data. *[http://dx.doi.org/10.1016/j.jmoldx.2016.07.003]

Research Associate — BioinformaticianResearch Associate — Bioinformatician
Nov 2014 - Oct 2016 · 2 yrsNov 2014 - Oct 2016 · 2 yrs

Performance of a MiSeq library preparation assay for PCR-based quantification of donor-derived cell-free DNA independent of prior genotyping of donor/recipient pairs in transplant patients. Bioinformatic data analysis and visualization of NGS output, including secondary and tertiary pipeline development and computational biology research.Performance of a MiSeq library preparation assay for PCR-based quantification of donor-derived cell-free DNA independent of prior genotyping of donor/recipient pairs in transplant patients. Bioinformatic data analysis and visualization of NGS output, including secondary and tertiary pipeline development and computational biology research.

Final test result is averaged from numerous pass-QC SNP (n=~100-130) where the recipient is homozygous, revealing informative donor allele frequencies .Final test result is averaged from numerous pass-QC SNP (n=~100-130) where the recipient is homozygous, revealing informative donor allele frequencies .

Undergraduate & Graduate Student ResearcherUndergraduate & Graduate Student Researcher
University of California, Santa CruzUniversity of California, Santa Cruz Aug 2011 - Jun 2014 · 2 yrs 11 mosAug 2011 - Jun 2014 · 2 yrs 11 mos Santa Cruz, CASanta Cruz, CA

Contributed to research projects in three molecular biology labs at UCSC:

- Single-cell RNA-Seq of an in vitro human breast cancer cell line with developed drug resistance & RNAi transfection knockdown of putative driver targets (with Dr. Nader Pourmand, Prof. Biomolecular Engineering, UCSC Genome Sequencing Center, Nanotechnology Gene Applications)
[M.Sc. Graduate Capstone project, 2014]

- Nanopore sequencing & potential application of heterologous protein expression in halophilic archaea yielding highly salt tolerant DNA Polymerase motors (with Dr. Mark Akeson, Prof. & Chair Biomolecular Engineering)
[Undergraduate Senior Thesis, 2012-2013]

- Recombinant chromosome protein dynamics during meiotic cell-cycle regulation and dysfunction in C. elegans (with Dr. Needhi Bhalla, Assoc. Prof. Genetics, Molecular Cellular and Developmental Biology)
[Undergraduate research, chemical mutagenesis & manual LoF/GoF genetic screening via genome engineered epistatic phenotype markers, 2011-2013]Contributed to research projects in three molecular biology labs at UCSC: - Single-cell RNA-Seq of an in vitro human breast cancer cell line with developed drug resistance & RNAi transfection knockdown of putative driver targets (with Dr. Nader Pourmand, Prof. Biomolecular Engineering, UCSC Genome Sequencing Center, Nanotechnology Gene Applications) [M.Sc. Graduate Capstone project, 2014] - Nanopore sequencing & potential application of heterologous protein expression in halophilic archaea yielding highly salt tolerant DNA Polymerase motors (with Dr. Mark Akeson, Prof. & Chair Biomolecular Engineering) [Undergraduate Senior Thesis, 2012-2013] - Recombinant chromosome protein dynamics during meiotic cell-cycle regulation and dysfunction in C. elegans (with Dr. Needhi Bhalla, Assoc. Prof. Genetics, Molecular Cellular and Developmental Biology) [Undergraduate research, chemical mutagenesis & manual LoF/GoF genetic screening via genome engineered epistatic phenotype markers, 2011-2013]

Ratcheting of voltage-driven long single-molecule DNA through a membrane-bound aperture of alpha hemolysin nanopore: control by a DNA Polymerase motor to resolve the signal-to-noise ratio of electrical output down to base-identifiable timeframesRatcheting of voltage-driven long single-molecule DNA through a membrane-bound aperture of alpha hemolysin nanopore: control by a DNA Polymerase motor to resolve the signal-to-noise ratio of electrical output down to base-identifiable timeframes

EducationEducation

University of California, Santa CruzUniversity of California, Santa Cruz
Master of Science (M.S.), Biomolecular Engineering and BioinformaticsMaster of Science (M.S.), Biomolecular Engineering and Bioinformatics 2013 - 20142013 - 2014

University of California, Santa CruzUniversity of California, Santa Cruz
Bachelor of Science (B.S.), BioengineeringBachelor of Science (B.S.), Bioengineering 2009 - 20132009 - 2013

SkillsSkills

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BioinformaticsBioinformatics

Bioinformatics Data Scientist at PACT PharmaBioinformatics Data Scientist at PACT Pharma

Endorsed by 3 colleagues at University of California, Santa CruzEndorsed by 3 colleagues at University of California, Santa Cruz

8 endorsements8 endorsements

PythonPython

Passed LinkedIn Skill AssessmentPassed LinkedIn Skill Assessment

Endorsed by 2 colleagues at University of California, Santa CruzEndorsed by 2 colleagues at University of California, Santa Cruz

6 endorsements6 endorsements

Molecular BiologyMolecular Biology

Endorsed by Brian Christie and 1 other who is highly skilled at thisEndorsed by Brian Christie and 1 other who is highly skilled at this

Endorsed by 3 colleagues at University of California, Santa CruzEndorsed by 3 colleagues at University of California, Santa Cruz

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PublicationsPublications

A High-throughput Platform To Produce NeoE-HLA Libraries For Capturing NeoE-specific T Cells From Peripheral Blood Of Patients With Solid TumorsA High-throughput Platform To Produce NeoE-HLA Libraries For Capturing NeoE-specific T Cells From Peripheral Blood Of Patients With Solid Tumors
AACR 2020 · Jan 1, 2020AACR 2020 · Jan 1, 2020

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We have developed a high-throughput, automated process for the cloning, expression and purification of soluble proteins
comprising neoE peptide with beta2m and the HLA heavy chain fused into a single polypeptide (neoE-HLA proteins). Of the 13
thousand HLA alleles in the human population, with 6 HLA alleles expressed in each person, our HLA catalog enables rapid
production of neoE-HLA protein candidate libraries, representing >4 of 6 HLAs coverage for ≥95% of all patients, regardless of
ethnicity. We can generate neoE-HLA libraries in single production runs for up to several hundred neoE-HLA candidates per
patient. These soluble neoE-HLA proteins are then assembled into barcoded “snare libraries” for the interrogation of matched
PBMCs from that patient for CD8 T cells that specifically bind the cognate neoE-HLA tumor targets. The imPACT Isolation
Technology® is capable of capturing CD8 T cells of a frequency as low as 1 per 106.
This platform is capable of producing snares predicted to represent high affinity (≤500 nM) and low affinity (>500nM) neoE
binding to their corresponding HLAs. These capabilities permit deep and broad interrogation of the tumor-targeted neoE-specific
T cell repertoire in the blood of patients with solid cancers as exemplified here. In summary, our proprietary neoE-HLA prediction
pipeline and the proprietary high-throughput neoE-HLA protein production platform enables the discovery of tumor-targeted,
neoE-specific T cells from patients. These breakthrough technologies support the on-going Phase 1 clinical trial of personalized
engineered autologous NeoTCR-P1 T cell therapies for patients with eight different solid tumor types (NCT03970382).We have developed a high-throughput, automated process for the cloning, expression and purification of soluble proteins comprising neoE peptide with beta2m and the HLA heavy chain fused into a single polypeptide (neoE-HLA proteins). Of the 13 thousand HLA alleles in the human population, with 6 HLA alleles expressed in each person, our HLA catalog enables rapid production of neoE-HLA protein candidate libraries, representing >4 of 6 HLAs coverage for ≥95% of all patients, regardless of ethnicity. We can generate neoE-HLA libraries in single production runs for up to several hundred neoE-HLA candidates per patient. These soluble neoE-HLA proteins are then assembled into barcoded “snare libraries” for the interrogation of matched PBMCs from that patient for CD8 T cells that specifically bind the cognate neoE-HLA tumor targets. The imPACT Isolation Technology® is capable of capturing CD8 T cells of a frequency as low as 1 per 106. This platform is capable of producing snares predicted to represent high affinity (≤500 nM) and low affinity (>500nM) neoE binding to their corresponding HLAs. These capabilities permit deep and broad interrogation of the tumor-targeted neoE-specific T cell repertoire in the blood of patients with solid cancers as exemplified here. In summary, our proprietary neoE-HLA prediction pipeline and the proprietary high-throughput neoE-HLA protein production platform enables the discovery of tumor-targeted, neoE-specific T cells from patients. These breakthrough technologies support the on-going Phase 1 clinical trial of personalized engineered autologous NeoTCR-P1 T cell therapies for patients with eight different solid tumor types (NCT03970382).

Other authorsOther authors

Validation of a Clinical-Grade Assay to Measure Donor-Derived Cell-Free DNA in Solid Organ Transplant PatientsValidation of a Clinical-Grade Assay to Measure Donor-Derived Cell-Free DNA in Solid Organ Transplant Patients
The Journal of Molecular Diagnostics · Jul 8, 2016The Journal of Molecular Diagnostics · Jul 8, 2016

Show publication

The use of circulating cell-free DNA (cfDNA) as a biomarker in transplant recipients offers advantages over invasive tissue biopsy as a quantitative measure for detection of transplant rejection and immunosuppression optimization. This noninvasive clinical-grade sequencing assay can be completed within 3 days, providing the practical turnaround time preferred for transplanted organ surveillance.The use of circulating cell-free DNA (cfDNA) as a biomarker in transplant recipients offers advantages over invasive tissue biopsy as a quantitative measure for detection of transplant rejection and immunosuppression optimization. This noninvasive clinical-grade sequencing assay can be completed within 3 days, providing the practical turnaround time preferred for transplanted organ surveillance.

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CoursesCourses

Programming in Python Programming in Python
BME 160BME 160

Associated with University of California, Santa CruzAssociated with University of California, Santa Cruz

Advanced Biochemistry: Protein and Nucleic Acids Structure and FunctionAdvanced Biochemistry: Protein and Nucleic Acids Structure and Function
CHEM 200B/CCHEM 200B/C

Associated with University of California, Santa CruzAssociated with University of California, Santa Cruz

Advanced Molecular BiologyAdvanced Molecular Biology
BIOL 200BBIOL 200B

Associated with University of California, Santa CruzAssociated with University of California, Santa Cruz

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