Mao-Jan Lin

Mao-Jan Lin

PhD student at Johns Hopkins University

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Measuring, visualizing, and diagnosing reference bias with biastools

Published in Genome Biology, 2024

Recent alignment methods aim to reduce reference bias, where reads with non-reference alleles fail to align correctly. However, there is a lack of methods for analyzing reference bias. We present biastools, which measures and categorizes reference bias.

Recommended citation: Lin, Mao-Jan, et al. "Measuring, visualizing, and diagnosing reference bias with biastools." Genome Biology 25.1 (2024): 101.
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Profiling genes encoding the adaptive immune receptor repertoire with gAIRR Suite

Published in Frontiers in Immunology, 2022

gAIRR-Suite is a new toolkit profiling genes encoding the Adaptive Immune Receptor Repertoire (AIRR). We combined experimental and computational methods to genotype both documented and novel V, D, J genes in the human germline genome.

Recommended citation: Lin, Mao-Jan, et al. "Profiling genes encoding the adaptive immune receptor repertoire with gAIRR Suite." Frontiers in Immunology 13 (2022): 922513.
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Preprints

IGLoo: Profiling the Immunoglobulin Heavy chain locus in Lymphoblastoid Cell Lines with PacBio High-Fidelity Sequencing reads

Published in bioRxiv, 2024

The Lymphoblastoid Cell Lines (LCLs) is a mixture of germline and somatically recombined haplotypes in the immunoglobulin (IG) gene regions, making them hard to profile and assemble. IGLoo is a toolkit to profile the IG heavy chain (IGH) locus in LCLs with HiFi reads. It can also improve the assembly quality in the IGH locus.

Recommended citation: Lin, Mao-Jan, Ben Langmead, and Yana Safonova. "IGLoo: Profiling the Immunoglobulin Heavy chain locus in Lymphoblastoid Cell Lines with PacBio High-Fidelity Sequencing reads." bioRxiv (2024).
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Conference Papers

Hardware Accelerator Design for Dynamic-Programming-Based Protein Sequence Alignment with Affine Gap Tracebacks

Published in 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2019

We propose an ASIC design for dynamic programming sequence alignment with affine-gap traceback. The architecture reduce memory usage for traceback by 25%. The design speed up pairwise alignment by 570x

Recommended citation: Lin, Mao-Jan, Yu-Cheng Li, and Yi-Chang Lu. "Hardware accelerator design for dynamic-programming-based protein sequence alignment with affine gap tracebacks." 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2019.
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