Update List of tools.rmd

List of tools.rmd

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 Sequencing microbial DNA from diverse environments is now more democratized than before. Serval research groups have now started to use this powerful tool in their research projects. As a beginner, the entire process from sample collection to analysis for sequencing data is a daunting task. More specifically, the downstream processing of raw reads is the most time consuming and mentally draining stage. It is vital to understand the basic concepts in microbial ecology and then to use various tools at disposal to address specific research questions. Thankfully, several young researchers supported by their experienced principal investigators/supervisors are working on creating various tools for analysis and interpretation of microbial community data. A major achievement of the scientific community is the open science initiative which has led to sharing of knowledge worldwide. For microbial community analysis, several tools have been created in R, a free to use (GNU General Public License) programming language(Team, 2000). The power of R lies in its ease of working with individuals lacking programming skills and easy sharing of analysis scripts codes and packages aiding reproducibility. Using tools such as QIIME (the newer QIIME2) (Caporaso, Kuczynski, Stombaugh et al., 2010), Mothur (Schloss, Westcott, Ryabin et al., 2009), DADA2 (Callahan, McMurdie, Rosen et al., 2016) one can get from raw reads to species × samples table (OTU or ASVs amplicon sequence variants as suggested recently (Callahan, McMurdie & Holmes, 2017)). In this post, numerous resources that can be helpful for analysis of microbiome data are listed in the table 1. This list may not have all the packages listed. Feel free to add those packages or links to web tutorials related to microbiome data, there is a [google sheet at this link for a list of tools](https://docs.google.com/spreadsheets/d/1am-UyDVBGDOgm6jVQ5FDXxmg24iriHqeBeul14HRb1g/edit?usp=sharing) which can be edited to include more tools. These are mostly for improved statistical analysis. These tools provide convenient options for data analysis and include several steps where the user has to make decisions. The work by [McMurdie PJ, Holmes S](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003531), [Weiss S](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-017-0237-y) and [Tsilimigras M.C. and Fodor A.A] (http://www.sciencedirect.com/science/article/pii/S1047279716300722) are useful resources to understand the data common to microbiome census. It can be tricky and frustrating in the beginning but patience and perseverance will be fruitful at the end.  Remember, if it is not challenging, then it would be no fun!
 
 Tools:
-1.	Ampvis	[Tools for visualising amplicon sequencing data](http://madsalbertsen.github.io/ampvis/)  
+1.	Ampvis2	[Tools for visualising amplicon sequencing data](https://madsalbertsen.github.io/ampvis2/)  
 2.	CCREPE	[Compositionality Corrected by PErmutation and REnormalization] (http://bioconductor.org/packages/release/bioc/html/ccrepe.html)  
 3.	DADA2	[Divisive Amplicon Denoising Algorithm](https://www.nature.com/nmeth/journal/v13/n7/full/nmeth.3869.html)  
 4.	DESeq2	[Differential expression analysis for sequence count data](https://www.bioconductor.org/packages/devel/bioc/vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html)