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Change Log

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog and this project adheres to Semantic Versioning.

[Unreleased] - yyyy-mm-dd

Here we write upgrading notes for brands. It's a team effort to make them as

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Long read references

• 2022 Accelerating minimap2 for long-read sequencing applications on modern CPUs
• 2021 Long-read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits
• 2021 Towards population-scale long-read sequencing
• 2020 Opportunities and challenges in long-read sequencing data analysis
• 2020 Long-read human genome sequencing and its applications
• 2019 Long-Read Sequencing Emerging in Medical Genetics
• 2019 A new era of long-read sequencing for cancer genomics
• [2019 Using long-read sequencing to detect imprinted DNA

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Awesome pharmacogenomics papers

• 2022 Automated Pharmacogenomic Reports for Clinical Genome Sequencing | github
• 2022 Pharmacogenomics decision support in the U-PGx project: Results and advice from clinical implementation across seven European countries
• 2021 Applying Next-Generation Sequencing Platforms for Pharmacogenomic Testing in Clinical Practice
• 2021 StellarPGx: A Nextflow Pipeline for Calling Star Alleles in Cytochrome P450 Genes
• 2021 Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data ✅ 🔐
• [2021 Cyrius: accurate CYP2D6 genotyping using whole-genome sequen

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Learn Clinical Bioinformatics 📚

• My personal list of recommendations of resources to study genomics bioinformatics and clinical bioinformatics

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Courses

1. Applied Computational Genomics - Aaron Quinlan

• This is just the best open source course I could find until now.

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Tutorials

• Variant Calling Pipeline using GATK4
• GATK4 Best Practice Nextflow Pipeline
• Variant calling in human whole genome/exome sequencing data
• Germline short variant discovery (SNPs + Indels)
• W8: Variant Calling with GATK - Day 1
• W8: Variant Calling with GATK - Day 2

Datasets

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import pandas as pd

file = "input.bed"
df = pd.read_csv(file, sep="\t", names=["chr", "start", "end", "interval", "score", "strand"])
df[["gene", "extra"]] = df["interval"].str.split("_", 1, expand=True)
df.drop(["interval", "score", "strand", "extra"], axis=1, inplace=True)
new_df = df.groupby("gene").agg({"chr":"unique", "start":min, "end":max})
new_df.reset_index(inplace=True)
new_df["chr"] = new_df["chr"].apply(lambda chr: chr[0])
new_df["start"] = new_df["start"].astype("str")

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WDL tutorials

• learn-wdl

Introducing the Learn WDL Course

• Step-by-step tutorials for writing pipelines in WDL.
• WDL - style_guide
• (How to) Execute Workflows from the gatk-workflows Git Organization
• DAVE TANG - Learning WDL
• Running a Bioinformatics Software Pipeline with Wdl/Cromwell
• 17. Hello World WDL Tutorial - Geraldine Van der Auwera

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#!/usr/bin/python3
from pyliftover import LiftOver
import pandas as pd
import argparse
import mapply
import sys
import os

mapply.init(

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Exomedepth:

$ awk -F"__" '$1=$1' OFS="\t" PAHC44_1_CDHS-17427Z-2274_sorted.bed | cut -f1,2,3,4 > qiaseq_PAHC44_1_CDHS-17427Z-2274_no_header.bed

DeviCNV:

$ echo -e "Amplicon_ID\tChr\tAmplicon_Start\tAmplicon_End\tInsert_Start\tInsert_End\tGene\tTranscript\tExon\tPool" > qiaseq_PAHC44_1_CDHS-17427Z-2274_no_header.devicnv.bed $ awk -F"__" '$1=$1' OFS="\t" PAHC44_1_CDHS-17427Z-2274_sorted.bed | awk '{ print $4"."$7"\t"$1"\t"$2"\t"$3"\t"$2"\t"$3"\t"$4"\t"$4"\t"$7"\t"Pool1}' >> qiaseq_PAHC44_1_CDHS-17427Z-2274_no_header.devicnv.bed

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Learn Git Branching

Introdução aos commits no Git

• Um commit em um repositório git registra uma fotografia (snapshot) de todos os arquivos no seu diretório. É como um grande copy&paste, mas ainda melhor!
• O Git tem por objetivo manter os commits tão leves quanto possível, de forma que ele não copia cegamente o diretório completo toda vez que você commita. Ele pode (quando possível) comprimir um commit como um conjunto de mudanças (ou um "delta") entre uma versão do seu repositório e a seguinte.
• O Git também mantém um histórico de quando ocorreu cada commit. É por isso que a maioria dos commits tem ancestrais acima de si -- que indicamos usando setas na nossa visualização. Manter a história é ótimo para todos que trabalham no projeto!
• Há muito para aprender, mas por enquanto pense nos commits como snapshots do seu projeto. Os commits são muito leves, e mudar de um para outro é extremamente rápido!
• Vejamos o que isso significa na prática. Abaixo, temos uma vis