Hannah Le

on the atlas · 31

• Nintil - From chaos, order: On the nature and measurement of biological aging1 savers
• Bio x ML Hackathon: Drive forward the next frontier of science and build on top of the latest foundation models like ESM-3 (98B parameters) and proprietary datasets. - Devpost1 savers
• 2022 Annual Letter - Chamath Palihapitiya4 savers
• Signature-scoring methods developed for bulk samples are not adequate for cancer single-cell RNA sequencing data | eLife1 savers
• Bias in RNA-seq Library Preparation: Current Challenges and Solutions1 savers
• A survey of best practices for RNA-seq data analysis | Genome Biology | Full Text1 savers
• nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.1 savers
• rnaseq » nf-core1 savers
• What Is Product Strategy? Framework & Examples2 savers
• Identifying and mitigating batch effects in whole genome sequencing data | BMC Bioinformatics | Full Text1 savers
• Nature Biotechnology’s academic spinouts of 2019 | Nature Biotechnology1 savers
• So where are we with deep learning for biochem? — lada nuzhna2 savers
• Non-Bio ppl sometimes ask me how to get up to speed with Biology. Most...3 savers
• Challenges and recommendations to improve the installability and archival stability of omics computational tools1 savers
• Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic1 savers
• To make the most rational decisions, use your gut — Ada Nguyen2 savers
• Biomedical Data Science: Mining and Modeling1 savers
• Nextflow + Containerization – NGS Analysis1 savers
• Nextflow Patterns1 savers
• Using prototyping to choose a bioinformatics workflow management system1 savers
• BioDepot/RNA-seq-lambda: Rapid RNA-seq pipeline using AWS lambda functions for alignmen1 savers
• Graph-based machine learning: Part I | by Sebastien Dery | Insight1 savers
• Highly accurate protein structure prediction with AlphaFold | Nature7 savers
• Negotiation Masterclass1 savers
• N=1 Collaborative for Individualized Medicine1 savers
• Mental model examples: How to actually use them | Elon Musk1 savers
• Winston Yan on Twitter: "First, rare disease isn't rare. Worldwide, you can fill a country the size of the US w/ those affected by rare disease (400M+). The problem is that they're diverse & heterogeneous, with tiny populations considered too rare for commercial opportunity. (h/t @GlobalGenes) (2/n) https://t.co/Uynu1UY8yy" / Twitter1 savers
• Google Calendar - Tuesday, February 8, 2022, today1 savers
• Online textbook: Computational Biology: Genomes, Networks, Evolution. MIT course 6.047/6.8781 savers
• Clay - Be more thoughtful with the people in your network.5 savers
• Architecting Discovery: A Model for How Engineers Can Help Invent Tools for Neuroscience9 savers

highlights · 39

• PCR amplification stochastically introduces biases, which can propagate to later cycles

  Bias in RNA-seq Library Preparation: Current Challenges and Solutions

• --featurecounts_feature_type

  rnaseq » nf-core

• --featurecounts_group_type

  rnaseq » nf-core

• Read filtering options

  rnaseq » nf-core

• --skip_markduplicates

  rnaseq » nf-core

• --save_align_intermeds

  rnaseq » nf-core

• Read trimming options

  rnaseq » nf-core

• Transcript assembly and quantification

  nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

• Create bigWig coverage files

  nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

• UMI-based deduplication

  nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

• UMI extraction (UMI-tools)

  nf-core/rnaseq: RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.

• Delighting your customers Planning for growth Differentiating your product from the competition

  What Is Product Strategy? Framework & Examples

• A well-researched product strategy provides companies with a clear sense of what niche their product will occupy when it goes to market.

  What Is Product Strategy? Framework & Examples

• a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype

  Identifying and mitigating batch effects in whole genome sequencing data | BMC Bioinformatics | Full Text

• new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect

  Identifying and mitigating batch effects in whole genome sequencing data | BMC Bioinformatics | Full Text

• detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.

  Identifying and mitigating batch effects in whole genome sequencing data | BMC Bioinformatics | Full Text

• on the hunt for other RNA-binding proteins that can be targeted with small molecules

  Nature Biotechnology’s academic spinouts of 2019 | Nature Biotechnology

• molecules that interfere with the Lin28–let-7 interaction, which they reasoned would be candidates for anticancer therapies

  Nature Biotechnology’s academic spinouts of 2019 | Nature Biotechnology

• Because let-7 is a tumor suppressor, this meant that Lin28, as a negative regulator of a tumor suppressor

  Nature Biotechnology’s academic spinouts of 2019 | Nature Biotechnology

• providing the list of these unstable positions for the two most recent builds (GRCh37 and GRCh38)

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• removing input data which overlap with the gapped regions

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• gapped regions in both chain files can result in conversion failure

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• 1% of SNVs did not convert from GRCh37 to GRCh38, and an average of 5% of SNVs did not convert from GRCh38 to GRCh37.

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• first exon of POTEG.

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• 19553586 on chromosome 14, where the reference allele is still T (chr14:c.19553586 T > A

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• Consider the T > A substitution at position 15690247 on chromosome 22 of GRCh38 (chr22:c.15690247 T > A), contained in the first exon of the POTEH gene.

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• there are a few SNVs whose coordinates in hg38 and hg19 … have inconsistent chromosome numbers

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• liftOver (provided as part of the Genome Browser tool [3] hosted by the UCSC Genomics Institute),

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• not all positions are comparable between builds

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• One of the major updates in GRCh38 was the closing of numerous gaps where sequencing had previously not been possibl

  Converting single nucleotide variants between genome builds: from cautionary tale to solution | Briefings in Bioinformatics | Oxford Academic

• Nextflow adopts a dataflow programming model whereby the processes are connected via their outputs and inputs to other processes, and processes run as soon as they receive an input

  Using prototyping to choose a bioinformatics workflow management system

• moderate-sized proteins due to the computational intractability of molecular simulation, the context dependence of protein stability and the difficulty of producing sufficiently accurate models of protein physics

  Highly accurate protein structure prediction with AlphaFold | Nature

• when you act like you’re giving a friend advice, you sidestep your flow paralysis.

  Mental model examples: How to actually use them | Elon Musk

• SIFT is a multistep procedure that, given a protein sequence, (1) searches for similar sequences, (2) chooses closely related sequences that may share similar function, (3) obtains the multiple alignment of these chosen sequences, and (4) calculates normalized probabilities for all possible substitutions at each position from the alignment.

  Predicting Deleterious Amino Acid Substitutions

• SIFT uses sequence homology to compute the likelihood that an amino acid substitution will have an adverse effect on protein function

  SIFT web server: predicting effects of amino acid substitutions on proteins

• VAEs have been successful in learning complex high-dimensional distributions across multiple domains including prediction of protein function

  Disease variant prediction with deep generative models of evolutionary data | Nature

• SIFT [6, 7], PolyPhen (v2) [8, 9], GERP++ [10, 11], Condel [12], CADD [13], fathmm [14], MutationTaster [15], MutationAssessor [16, 17], GESPA [18] and, more recently, REVEL [19].

  Variant effect prediction tools assessed using independent, functional assay-based datasets: implications for discovery and diagnostics | Human Genomics | Full Text

• We usually think of all these things as separate phenomena, and we have separate bodies of knowledge for reasoning about each. Yet all are answers to the question “What can you build with electrons, protons, and neutrons?”

  The varieties of material existence

• In the earliest days of optogenetics, we arrived at the core idea by going through the laws of physics systematically and thinking about what forms of energy (me- chanical, magnetic, optical, etc.) could be delivered to the brain and used to control neurons.

  Architecting Discovery: A Model for How Engineers Can Help Invent Tools for Neuroscience