Open Notebook: sequence

https://bioinformaticsreview.com/20170704/role-of-information-theory-chaos-theory-and-linear-algebra-and-statistics-in-the-development-of-alignment-free-sequence-analysis/

Square of Ajacency matrice give estimation of path

Quantum Walk and Graph search.

Qin: sequence alignment can be conerted all-2-all adjacent matrix. So, sequence alignment then might become a quantum walk and graph search problem.

Thursday, November 26, 2020

single cell DNA sequencing (genome sequencing)

=>QIAGEN single cell DNA library prep

https://www.qiagen.com/us/products/next-generation-sequencing/library-preparation/qiaseq-fx-single-cell-dna-library-kit/?cmpid=PC_GEN_single-cell-analysis-sales_0620_SEA_GA&clear=true#orderinginformation

The QIAseq FX Single Cell DNA Library kit provides a complete solution for whole genome sequencing from isolated single animal or bacterial cells or low amounts of genomic DNA. The kit includes all reagents required for cell lysis, whole genome amplification, enzymatic DNA fragmentation and PCR-free NGS library preparation. The kit provides comprehensive genome coverage and exceptional sequence fidelity, reducing false positives and minimizing drop-outs. The kit is ideally suited to the analysis of aneuploidy and copy number variation and sequence variation in single cells of for whole genome sequencing from rare samples

Monday, December 31, 2018

RPM, TPM, RPKM

from . https://www.biostars.org/p/273537/

In RNA-seq gene expression data analysis, we come across various expression units such as RPM, RPKM, FPKM and raw reads counts. Most of the times it's difficult to understand basic underlying methodology to calculate these units from mapped sequence data.

I have seen a lot of post of such normalization questions and their confusion among readers. Hence, I attempted here to explain these units in the much simpler way (avoided complex mathematical expressions).

Why different normalized expression units:

The expression units provide a digital measure of the abundance of transcripts. Normalized expression units are necessary to remove technical biases in sequenced data such as depth of sequencing (more sequencing depth produces more read count for gene expressed at same level) and gene length (differences in gene length generate unequal reads count for genes expressed at the same level; longer the gene more the read count).

Gene expression units and calculation:

1. RPM (Reads per million mapped reads)

For example, You have sequenced one library with 5 million(M) reads. Among them, total 4 M matched to the genome sequence and 5000 reads matched to a given gene.

Notes:

RPM does not consider the transcript length normalization.
RPM Suitable for sequencing protocols where reads are generated irrespective of gene length![enter image description

2. RPKM (Reads per kilo base per million mapped reads)

Here, 10^3 normalizes for gene length and 10^6 for sequencing depth factor.

FPKM (Fragments per kilo base per million mapped reads) is analogous to RPKM and used especially in paired-end RNA-seq experiments. In paired-end RNA-seq experiments, two (left and right) reads are sequenced from same DNA fragment. When we map paired-end data, both reads or only one read with high quality from a fragment can map to reference sequence. To avoid confusion or multiple counting, the fragments to which both or single read mapped is counted and represented for FPKM calculation.

For example, You have sequenced one library with 5 M reads. Among them, total 4 M matched to the genome sequence and 5000 reads matched to a given gene with a length of 2000 bp.

Notes:

RPKM considers the gene length for normalization
RPKM is suitable for sequencing protocols where reads sequencing depends on gene length
Used in single-end RNA-seq experiments (FPKM for paired-end RNA-seq data)

3. TPM (Transcript per million)

$A \cdot \frac{1}{\sum(A)} \cdot 10^6 \\ \\ \\ Where \ A = \frac{total \ reads \ mapped \ to \ gene \cdot 10^3}{gene \ length \ in \ bp}$

Notes:

TPM considers the gene length for normalization
TPM proposed as an alternative to RPKM due to inaccuracy in RPKM measurement (Wagner et al., 2012)
TPM is suitable for sequencing protocols where reads sequencing depends on gene length

References:

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature methods. 2008 Jul 1;5(7):621-8.
Wagner GP, Kin K, Lynch VJ. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory in biosciences. 2012 Dec 1;131(4):281-5.

Saturday, August 22, 2015

Strope 2015 Genome Research, 100 S. cerevisiae genomes, McCusker lab

http://genome.cshlp.org.proxy.library.emory.edu/content/25/5/762/suppl/DC1

Strope PK, McCusker group at Duke
2015 May;25(5):762-74. doi: 10.1101/gr.185538.114. Epub 2015 Apr 3.

Strope15GR used its own strain nominations, even though the strains are obtained from others. This is quite annoying.

I recognize M22, M1-2, YPS163

Strains are explained in Table S1, accession numbers are explained in table_S19.

Monday, September 29, 2014

reverse complementary sequences in R seqinr

## Show that comp() does *not* return the reverve complementary strand:

c2s(comp(s2c("aaaattttggggcccc")))

## Show how to get the reverse complementary strand:

c2s(rev(comp(s2c("aaaattttggggcccc"))))

## Show what happens with non allowed values:

c2s(rev(comp(s2c("aaaaXttttYggggZcccc"))))

## Show what happens with ambiguous bases:

allbases <- s2c("abcdghkmstvwn")

comp(allbases) # NA are produced

comp(allbases, ambiguous = TRUE) # No more NA

my code: _get_double_strand.R

setwd("~/github/ctls/sequences")

install.packages("seqinr")

library(seqinr);

list.files()

seqs = read.fasta("panda16srDNA.fasta")

str(seqs);

seqs[[1]]

length(seqs[[1]])

c2s( rev( comp(seqs[[1]]) ) )

c2s(seqs[[1]][1:120])

c2s(comp(seqs[[1]][1:120]))