@HachLab

@f_hach's lab. Based in Vancouver Prostate Centre. Computational algorithms for cancer genomics research. 🧬💻

 Vancouver, British Columbia
hachlab.org
Joined January 2019
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All of this is packaged into a neat package that comes with a handy Snakefile to run your simulation configs (including full UNIX piping support!). And the insulation of each module means that you can write your own TKSM module! Here is a list of currently implemented modules:
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These simulation pipelines can be more complex, with branches defined as their own linear pipelines! For example here is a scRNA-seq experiment that is made possbile by TSKM config:
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This design allows the user to define arbitrary simulation pipelines using easy-to-write YAML configs. For example, here is a simple bulk RNA-seq long-read pipeline and its corresponding YAML config:
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TKSM is a modular, easy-to-customize, and fast long-read simulator. Our design philosophy in TKSM is: 1. Each library prep step will be targeted by a TKSM module 2. The I/O of each core module is a description of nuclueic molecules that (eventually) will be reads
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Congratulations on all your hard work and well deserved degrees! Can't wait to see what you go on to achieve next 🎉🥂 (when you have four grads defending in a row [including three in the same week!] mid-day celebration is absolutely a must!)
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One of the most exciting parts of Freddie is its clustering/error-correction step which clusters long-reads while facing two challenges: 1) not knowing a priori how many isoform clusters exist 2) needing to account for correctable errors that result from sequencing
In Freddie, we formulate the clustering step as an optimizing problem that we coin MErCI (Minimum Error Clustering into Isoforms). As a result of the segmentation step, long-reads are summarized as segments. The clustering step runs in rounds. In each round reads are clustered into a "recycling" bin or an isoform bin based on their similarities/differences: a read in the recycling bin incurs a constant cost while ones in the isoform bin incur cost that depends on how many errors they contain. Recycling bin reads are used as input to next round of clustering.

ALT In Freddie, we formulate the clustering step as an optimizing problem that we coin MErCI (Minimum Error Clustering into Isoforms). As a result of the segmentation step, long-reads are summarized as segments. The clustering step runs in rounds. In each round reads are clustered into a "recycling" bin or an isoform bin based on their similarities/differences: a read in the recycling bin incurs a constant cost while ones in the isoform bin incur cost that depends on how many errors they contain. Recycling bin reads are used as input to next round of clustering.

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Freddie employs a multi-step approach to detect isoforms where each step is designed with high error-prone long RNA reads.
Freddie has three main stages: partitioning, segmentation, and Clustering/error-correction. After partitioning, different subsets of the input data can be processed in-parallel for faster computation.

ALT Freddie has three main stages: partitioning, segmentation, and Clustering/error-correction. After partitioning, different subsets of the input data can be processed in-parallel for faster computation.

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The paper introduces our new computational tool, scTagger which matches long- and short-reads from hybrid single cell sequencing data. This kind of lib protocols (and scTagger processing of it) should enable exploration of alternative splicing behavior at the single cell level!
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Ghazal in action at @RECOMBseq
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If you enjoyed the talk, make sure to check the iRNA special poster for more Q&A's!
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Today, our alumnus @haghshenas_e presented his last (but not least) of his PhD at @RECOMBseq on his hybrid assembly tool, HASLR! 📜: biorxiv.org/content/10.1101/… 💾: github.com/vpc-ccg/haslr 🐍: conda install -c bioconda haslr
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