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Fitting Linear Mixed-Effects Models Using lme4

Douglas M. Bates et al. · Journal of Statistical Software · 2015 · Lea fakapālangi

CC BY

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Love MI et al. · Genome biology · 2014 · Lea fakapālangi

CC BY 4.0

Trimmomatic: a flexible trimmer for Illumina sequence data

Bolger AM et al. · Bioinformatics (Oxford, England) · 2014 · Lea fakapālangi

CC BY 3.0

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

Moher D et al. · PLoS medicine · 2009 · Lea fakapālangi

CC BY 4.0

A new criterion for assessing discriminant validity in variance-based structural equation modeling

Jörg Henseler et al. · Journal of the Academy of Marketing Science · 2014 · Lea fakapālangi

CC BY 4.0

Highly accurate protein structure prediction with AlphaFold

Jumper J et al. · Nature · 2021 · Lea fakapālangi

CC BY 4.0

Cutadapt removes adapter sequences from high-throughput sequencing reads

Marcel Martin · EMBnet journal · 2011 · Lea fakapālangi

CC BY

limma powers differential expression analyses for RNA-sequencing and microarray studies

Ritchie ME et al. · Nucleic acids research · 2015 · Lea fakapālangi

CC BY 4.0

The ERA5 global reanalysis

Hans Hersbach et al. · Quarterly Journal of the Royal Meteorological Society · 2020 · Lea fakapālangi

CC BY 4.0

Features and development of Coot

Emsley P et al. · Acta crystallographica. Section D, Biological crystallography · 2010 · Lea fakapālangi

CC BY 2.0

lmerTest Package: Tests in Linear Mixed Effects Models

Alexandra Kuznetsova et al. · Journal of Statistical Software · 2017 · Lea fakapālangi

CC BY

MEGA11: Molecular Evolutionary Genetics Analysis Version 11

Tamura K et al. · Molecular biology and evolution · 2021 · Lea fakapālangi

CC BY 4.0

Welcome to the Tidyverse

Hadley Wickham et al. · The Journal of Open Source Software · 2019 · Lea fakapālangi

CC BY

PHENIX: a comprehensive Python-based system for macromolecular structure solution

Adams PD et al. · Acta crystallographica. Section D, Biological crystallography · 2010 · Lea fakapālangi

CC BY 2.0

WGCNA: an R package for weighted correlation network analysis

Langfelder P, Horvath S · BMC bioinformatics · 2008 · Lea fakapālangi

CC BY 2.0

Conducting Meta-Analyses in R with the metafor Package

Wolfgang Viechtbauer · Journal of Statistical Software · 2010 · Lea fakapālangi

CC BY

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Vijay Badrinarayanan et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2017 · Lea fakapālangi

CC BY 3.0

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

Langmead B et al. · Genome biology · 2009 · Lea fakapālangi

CC BY 2.0

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Li B, Dewey CN · BMC bioinformatics · 2011 · Lea fakapālangi

CC BY 2.0

Rayyan-a web and mobile app for systematic reviews

Ouzzani M et al. · Systematic reviews · 2016 · Lea fakapālangi

CC BY 4.0

HTSeq--a Python framework to work with high-throughput sequencing data

Anders S et al. · Bioinformatics (Oxford, England) · 2015 · Lea fakapālangi

CC BY 4.0

BLAST+: architecture and applications

Camacho C et al. · BMC bioinformatics · 2009 · Lea fakapālangi

CC BY 2.0

Model-based analysis of ChIP-Seq (MACS)

Zhang Y et al. · Genome biology · 2008 · Lea fakapālangi

CC BY 2.0

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

Moher D et al. · Systematic reviews · 2015 · Lea fakapālangi

CC BY 4.0

Lau fakataha ʻa e tupuʻanga mo e liliu

Fitting Linear Mixed-Effects Models Using lme4

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

Trimmomatic: a flexible trimmer for Illumina sequence data

Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement

A new criterion for assessing discriminant validity in variance-based structural equation modeling

Highly accurate protein structure prediction with AlphaFold

Cutadapt removes adapter sequences from high-throughput sequencing reads

limma powers differential expression analyses for RNA-sequencing and microarray studies

The ERA5 global reanalysis

Features and development of Coot

lmerTest Package: Tests in Linear Mixed Effects Models

MEGA11: Molecular Evolutionary Genetics Analysis Version 11

Welcome to the Tidyverse

PHENIX: a comprehensive Python-based system for macromolecular structure solution

WGCNA: an R package for weighted correlation network analysis

Conducting Meta-Analyses in R with the metafor Package

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Rayyan-a web and mobile app for systematic reviews

HTSeq--a Python framework to work with high-throughput sequencing data

BLAST+: architecture and applications

Model-based analysis of ChIP-Seq (MACS)

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

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