Ben Hitz @bonscotthoughts

@anshulkundaje @sinabooeshaghi no, colored pencils

@jmschreiber91 n

@JD_Buenrostro Even snowflakes can be classified: its.caltech.edu/~atomic/snowcr…

@arjunrajlab Still it's clear that a "type" is an ensemble of states. Types should probably be mapped to known microscopic evidence; that is mostly how they are thought of anyway. It starts to get fuzzy when you consider sorting cells on surface markers.

@arjunrajlab This isn't a useful definition of state because it's not precise. How many tpm difference is a different state? It is not a useful definition for type because cells have multiple functions (and function itself is slippery) and they generally are not found in all combinations

@lpachter Maybe you shouldn't read articles from profit focused paper mills like ...checks notes... Nature

@danorlovsky7 What app are you using?

1/ One of the most common mistakes in bioinformatics is the one-off error. If you are not careful, you will make mistakes. Even experienced bioinformaticians are not aware of it and the mistake prevails in many bioinformatics tools. A thread 🧵

@Aella_Girl @snowanddrugs2 is this because Republican-coded laws can be avoided more easier if you just have the resources to do so? Which is why dem-coded laws I consider more egalitarian.

@akhivae How many nations did Franco's Spain invade?

@Aella_Girl actual geneticists study the specific effect of human genetic variation on a huge variety of traits, diseases and phenotypes; see. e.g.: ebi.ac.uk/gwas/

@DrChrisCombs Honestly thought this was an orange cat wearing a very fancy drone hat

@GBBranstetter That's not fair. We aren't all tranq addicts

@pranamanam Aren't all these methods essentially bad? Better than random sure, but...

One funny story about this: I spent hours creating a figure in my book explaining 0 versus 1-based indexing and closed versus right-open intervals. The illustrator thought I made a careless error in starting one from 0 and the other from 1, and changed them to match 😱

James Pitt @Sahelanth

2 years ago

@vsbuffalo @jgschraiber One of the many terrifying things I learned from your Bioinformatics Data Skills book!

2 2 9 6K 7

Dear big consortia, It is never too late to be brave and use all that visibility you have to make a strong statement that academia will not be held hostage by glam journals & their shiny JIFs 1/

@zenahitz @lastpositivist what am I your google monkey? en.wikipedia.org/wiki/One_Crew_…

@Caroline_Bartma SPI1 actually official gene name.

Our paper "A machine readable specification for genomics assays" is now published in Bioinformatics, @OUPBioinfo. In short, we present a lightweight file format and command-line tool to document the structure of sequencing reads. Coauthored with @XiChenUoM and @lpachter. Paper: doi.org/10.1093/bioinf… Code: github.com/pachterlab/seq… What is in my sequencing reads? Sequencing machines produce text files, called FASTQs, that contain reads or sequences of DNA molecules. Assay developers and data generators deeply understand the contents of their reads; they know the location and presence biological and synthetic constructs like cellular barcodes. Collaborators, reproducers, and other scientists may not despite their sometimes obscure addition to supplementary material. Take for example the @10xGenomics Multiome assay. The 10x Genomics documentation [1] spells out the read structure for each modality: RNA reads contain a synthetic 16bp barcode, a 12 bp "randomly" generated unique molecular barcode (UMI), as well as cDNA that was captured via polyA capture. The ATAC reads consist of genomic DNA and a 16bp cellular barcode. However the 10x Genomics website explains that the ATAC portion of the 10x Multiome data contains an little-known 8bp constant sequence spacer that proceeds the 16bp cell barcode. So saying that you have "10x Multiome" reads is a necessary but not sufficient condition to know the contents of your FASTQ reads. The reason is because the FASTQ read structure is dependent on both the assay as well as the sequencing machine/recipe used; a sequencing library produced from one single-cell assay can yield different read structures depending these parameters. Take the the 10x Multiome assay. The ATAC 24bp barcode + spacer is usually sequenced as the i5 index read. Since the NextSeq 500/550 does not support a 24bp i5 read, the user must specify "dark cycles" (10x details the impact of this [2]) to skip the 8bp spacer. This yields a 16bp cell barcode in the i5 FASTQ file. If, however, the 10x Multiome ATAC library was not sequenced with dark cycles then the i5 FASTQ file will contain a 24bp spacer + barcode. I was originally unaware of the 8bp spacer and the use of dark cycles in Multiome library sequencing. But as I was recently looking ATAC reads I realized the impact it had on my count matrices; I had been extracting half of the cell barcode and all of the spacer. This meant I was performing barcode error correction and was UMI collapsing the, mostly similar, cell barcodes to produce few counts. This decoupling of read structure between the sequencing machine and the assay places a high priority on documenting read structure in a sequencer and assay-specific manner so that preprocessing tools can accurately extract and process relevant sequenced elements. A machine-readable specification I was inspired by @XiChenUoM 's efforts (which started while in @teichlab) in documenting sequencing reads of assays and I came up with an idea to document read structure in a machine- and human-readable specification. The specification is called seqspec [3]. The specification details the structure of a YAML file that allows users to specify and annotate the types of sequences that are contained in their FASTQ data. seqspec uses a nested representation of "Regions" and "Reads" that allows users to annotate groups of sequenced elements and map sequencing reads to sequencing primers. This enables, for example, all of the elements contained in Read 1 of a FASTQ file, such as the barcode and UMI in the 10x RNA assay to be annotated as belonging to Read 1. The spec also comes with an accompanying seqspec command line tool which gives users who annotate their sequencing assays many benefits: 1. Reproducibility and verifiability of the assay structure 2. Positional extraction of relevant features 3. Visualization of the sequencing structure The seqspec command line tool makes it straightforward to extract the positional index of sequenced elements. The barcodes in the 10x Multiome dataset could have easily been identified as starting 8bp into the reads with the seqspec index command. The tool also also makes it straightforward to visualize the structure of your sequencing reads. seqspec print can produce publication-ready figure of your read structure. Most importantly, seqspec makes it easy for others to reanalyze data for which a seqspec exists, bringing about verifiability of analysis results. seqspec adoption seqspec aims to make genomics processing correct and reproducible. seqspec was recently adopted as the first standard in the @IGVFConsortium and we anticipate the publication of terabytes of sequencing data alongside their seqspec read annotations. I personally believe seqspec will be transformative for reproducibility and analysis efforts, in particular for those undertaken by consortia. I hope that public databases (like the @NCBI SRA/GEO and DDBJ) will test out seqspec and look to adopt it as a standard for data submission. seqspec is freely available, open source, open to contributions, useable, and well documented. Please take a look at the GitHub repo and try it out! We welcome feedback. [1] 10xgenomics.com/support/single… [2] kb.10xgenomics.com/hc/en-us/artic… [3] github.com/pachterlab/seq…

@RyanMarino As someone with a colonoscopy scheduled Friday I wonder the same thing