![]() ![]() Ideally, and the Hadfield et al paper talks about this, you want to use datasets of the same cell type, and have around 6 replicates. if (!requireNamespace("BiocManager", quietly = TRUE))īiocManager::install("devtools") # only if devtools not yet installedīiocManager::install("pachterlab/sleuth")įor the sake of building and testing this pipeline, we used 3 sample sets, where one is a tumor sample. Any way, I guess statisticians love it for their own reasons, so we’ll use it. ![]() Unfortunately, Sleuth is written in R and I hate R. But instead we went with Sleuth, which is also made by the Pachter Lab. These can be derived from TPM, so we can skip that for now and move on with our analysis, and the hard/not fun part for me, as shown in Fig1 after Kallisto we move to TXI, TMM, DESq2, etc. There are other popular units of measurement like RPKM/FPKM, reads per kilobase per million reads mapped. TPM simply shows the rate of counts per base (X i/l i) where we get a measurement of the proportion of transcripts in the pool of RNA, here it is in math. This is very basic, so there is a more statistically relevant number included in the file, Transcripts Per Million (TPM), which is something people love. ![]() This has our estimated counts of the number of our RNA-seq reads matched to their respective gene transcripts, essentially the more reads that are at a given gene the more that gene is being expressed in our sample/given cell. If we look in the output folder we can find the " abundance.tsv" file. Kallisto quant -i transcripts.idx -o output -b 100 reads_1.fastq.gz reads_2.fastq.gzīelieve it or not, at this point Kallisto’s work is done. We can also build our own, after which there’s only one command to do the first part of our analysis. The Pachter Lab provides several pre-built transcriptomes here including H.Sapiens. This is very similar to the reference genome used in DNA analysis pipelines. Once Kallisto is installed, it will need a transcriptome index. Ideally there’s some pre-processing that should be done on the FASTQ files with our RNA-seq data before jumping into the analysis but let’s leave that up to someone else to explain. NetBSD, RHEL/CentOS: pkgin install kallisto ![]() Everything you see on this post was done on Linux AWS instances. Depending on your OS one of the following commands will install it. There are new algorithms and tools coming out all the time, but Kallisto by Páll Melsted and Lior Pachter seems to be the winner for now. If we look at Fig1 above, we see three separate sets of algorithms, pipelines, to go from our raw data (FASTQ files) to our finished answers, we will focus on method ( B).Īlignment-free analysis methods are a relatively new breakthrough, and allows us to take our sequencing data coming out of the machines, and skip over the worst part. As with DNA analysis, sequence alignment is the most time and resource consuming step. Because the same machines by Illumina, PacBio, Oxford Nanopore, etc, are used to generate RNA sequencing data, and we need many more reads to get confident pictures of what’s happening across cells, DGE tends to be computationally expensive. So, RNA sequencing has become more and more popular however, trying to make sense of the data and actually understand what it is our machines are picking up has introduced a whole suite of challenges to overcome.ĭifferential Gene Expression ( DGE) is currently the most common use for RNA-seq, where we try to find out which genes from our DNA are expressed differently across cell or sample types as RNA. Of course, the answers are most likely in RNA, as the DNA is our permanent record, and the RNA is what is being worked with at any given moment. More and more, as we begin to get a solid grasp on DNA sequencing people are finding the need to understand what makes each type of cell different, or what changes occur before/after the introduction of a therapeutic. ![]()
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