6

One of the 2015 papers from the 1000 genomes project has a nice figure (figure 1) showing the size distribution of medium to large sized insertions and deletions: From another 2015 1000 genomes paper, one can see that the absolute number of smaller indels is much larger, though an exact size range isn't given (as far as I saw). If you really want to know ...


5

Genome-In-A-Bottle (GIAB; version 3.3.2) contains 3.21M SNPs on auto+X chromosomes and 0.51M INDELs in 2.58Gb confident regions. The ins:del ratio is 0.92. On the CHM1-CHM13 pacbio assembly (European ancestry), there are 3.57M auto+X SNPs and 0.58M INDELs in 2.71Gb confident regions [reference] with an ins:del ratio 0.99. These give you an idea of relative ...


4

Here are a couple books I'd recommend: Dan Gusfield's Algorithms on Strings, Trees, and Sequences is a deep and wide treatment of aligning, searching, and processing strings, trees, and sequences. Warren Ewens' Statistical Methods in Bioinformatics devotes a chapter to BLAST and the math underneath it. Edit - Another book that may be useful for some ...


4

Over on biostars there's a thread like this every year or so. I'll link to the 2016 edition and the (much shorter) 2015 edition. My personal picks from those would be: ExAC salmon, which is now published kallisto, which is also now published


4

As I understand, the software tool Lambda is a viable, yet lesser known alternative to BLAST in the context of taxonomic classification of NGS data.


4

Besides generic nucleotide aligners, there are also more specialized tools for the alignment of 16S amplicon sequences, e.g. SINA (article, software) which is part of SILVA


4

My personal approach to something like this is to first try to reproduce existing work in order to learn to use relevant tools and understand the concepts involved. Then as you get more comfortable with the subject matter, you may spot an opportunity to add value. If you'd like to get started with the phylogenomics side of things, you can use this public ...


3

In my experience, it is more common to use all clinical data as-is for clinical studies. And if data is missing, either omit the sample or omit the variable with missing data. If your classifier can't handle the wide variation commonly seen in clinical studies then you may want to use a classifier which is less impacted by outliers.


3

After getting it out on paper (so to speak) I was able to accomplish what I wished with bash: #!/bin/bash gene_list=($(cat ./markers_clean.txt)) query=${gene_list[@]:0:1} parens="(" for gene in ${gene_list[@]:1} do query="${query} OR ${gene})" parens="${parens}(" done echo "${parens}${query} AND olfactory bulb)" With the output ...


3

If you're interested in variant calling, I just co-authored an O'Reilly book called Genomics in the Cloud that covers the GATK Best Practices from a scientific standpoint (germline and somatic short variants + somatic copy number alterations), as well as technical considerations like how to run it efficiently at scale (that's where the cloud part comes in). ...


2

Excellent question! I would recommend that you start with an NIH resource at the National Library of Medicine (NLM). PubMed (https://pubmed.ncbi.nlm.nih.gov/) provides search capabilities across the biomedical literature. You can also create an account and use it to manage your searches of the literature. An example search below for "protein misfolding ...


2

Please note that Cuvid19 is COVID-19, the WHO nomenclature. SARS2 is specifically called SARS-CoV-2 and is the virologists name for COVID-19 because that is the nomenclature of the International Committee on the Taxonomy of Viruses (ICTV) for the same virus following formal taxonomic investigation. It is important not to get the names confused. Technically ...


2

Bioinformatics and Functional Genomics by Jonathan Pevsner is a good one, he goes over the biology as well as how the algorithms work, and provides real-world examples.


2

Yes, there is a tool to do this called R2C2 by the Vollmers lab at UC Santa Cruz. https://www.biorxiv.org/content/early/2018/06/04/338020


2

A simple approach is to search for the gene name in NCBI Gene portal but this can miss a few cases and often include genes that are unrelated because the gene symbol is used by other unrelated genes as well. For a cleaner dataset, you can use the NCBI Orthologs as follows. Search for human ALDP in the NCBI homepage with All Databases selected in the ...


2

As mentioned in the comments, your biological question is going to be vitally important in determining the best way to tackle this issue. However, there are some things that you should generally keep in mind. Most de novo assemblers will perform kmer normalization. This means that the merged RNA-Seq data will be normalized across all libraries. This is ...


1

Removing outliers is common practice in statistical modeling and perfectly acceptable. However, with regards 1.5 IQR I am far from certain about this approach. Normally, if you want to be conservative then 3 standard deviations (SD) denote an outlier, which is more stringent than IQR. Some use 2 SD. If the value is lower than 2 SD from the group mean it isn'...


1

Bioinformatics is a big and very heterogeneous field with a lot of variation, so it's hard to recommend something that covers everything. That said, I think the best you'll do is to build familiarity with some of the common tools with some canned analyses. One possible option would be some of the scRNA-seq workflows from the Pachter group, they seem to be ...


1

From Wikipedia: In the mathematical field of dynamical systems, an attractor is a set of numerical values toward which a system tends to evolve, for a wide variety of starting conditions of the system. In biology you can think of an attractor as a state towards the system is moving to. A good example is the process of differentiation where stem cells ...


1

Rajarshi Guha describes the formulas to calculate Moreau Broto autocorrelation, Moran coefficient and Geary coefficient and has a bibliography. But why reinvent the wheel? It seems that the Autocorrelation module of the PyBioMed package does already what you want: it is a pure Python implementation without any external dependencies.


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