dkoslicki

ARK

Aggregation of kmers: preprocessing step for kmer-based metagenomics taxonomic profiling techniques

CAMIKraken

A Dockerfile and wrappers for the Kraken metagenomic classifier

CMash

Fast and accurate set similarity estimation via containment min hash

EMDeBruijn

Earth mover's distance between de Bruijn graphs

EMDUnifrac

Computation of Unifrac using Earth Movers Distance

MetaPalette

Metagenomic profiling and phylogenetic distances via common kmers

MinHashMetagenomics

Fast approximation of similarity for sets of very different sizes

NCATS

PressPurt

The core algorithm for the Koslicki & Novak JMB paper (2018)

Quikr

Quikr: a method for rapid reconstruction of bacterial communities via compressive sensing.

dkoslicki/CMash

Fast and accurate set similarity estimation via containment min hash

dkoslicki/MetaPalette

Metagenomic profiling and phylogenetic distances via common kmers

dkoslicki/MinHashMetagenomics

Fast approximation of similarity for sets of very different sizes

dkoslicki/EMDUnifrac

Computation of Unifrac using Earth Movers Distance

dkoslicki/NCATS

dkoslicki/PressPurt

The core algorithm for the Koslicki & Novak JMB paper (2018)

dkoslicki/TAMPA

A collection of tools to visualize CAMI profiling outputs

dkoslicki/CAMIARKQuikr

The ARK+Quikr algorithm in a format for the CAMI competition

dkoslicki/MedianUnifrac

Computing medians with respect to the UniFrac metric

dkoslicki/MultiresolutionBloomFilters

dkoslicki/swirl_courses

:mortar_board: A collection of interactive courses for the swirl R package.

dkoslicki/Assemblies-of-putative-SARS-CoV2-spike-encoding-mRNA-sequences-for-vaccines-BNT-162b2-and-mRNA-1273

RNA vaccines have become a key tool in moving forward through the challenges raised both in the current pandemic and in numerous other public health and medical challenges. With the rollout of vaccines for COVID-19, these synthetic mRNAs have become broadly distributed RNA species in numerous human populations. Despite their ubiquity, sequences are not always available for such RNAs. Standard methods facilitate such sequencing. In this note, we provide experimental sequence information for the RNA components of the initial Moderna (https://pubmed.ncbi.nlm.nih.gov/32756549/) and Pfizer/BioNTech (https://pubmed.ncbi.nlm.nih.gov/33301246/) COVID-19 vaccines, allowing a working assembly of the former and a confirmation of previously reported sequence information for the latter RNA. Sharing of sequence information for broadly used therapeutics has the benefit of allowing any researchers or clinicians using sequencing approaches to rapidly identify such sequences as therapeutic-derived rather than host or infectious in origin. For this work, RNAs were obtained as discards from the small portions of vaccine doses that remained in vials after immunization; such portions would have been required to be otherwise discarded and were analyzed under FDA authorization for research use. To obtain the small amounts of RNA needed for characterization, vaccine remnants were phenol-chloroform extracted using TRIzol Reagent (Invitrogen), with intactness assessed by Agilent 2100 Bioanalyzer before and after extraction. Although our analysis mainly focused on RNAs obtained as soon as possible following discard, we also analyzed samples which had been refrigerated (~4 ℃) for up to 42 days with and without the addition of EDTA. Interestingly a substantial fraction of the RNA remained intact in these preparations. We note that the formulation of the vaccines includes numerous key chemical components which are quite possibly unstable under these conditions-- so these data certainly do not suggest that the vaccine as a biological agent is stable. But it is of interest that chemical stability of RNA itself is not sufficient to preclude eventual development of vaccines with a much less involved cold-chain storage and transportation. For further analysis, the initial RNAs were fragmented by heating to 94℃, primed with a random hexamer-tailed adaptor, amplified through a template-switch protocol (Takara SMARTerer Stranded RNA-seq kit), and sequenced using a MiSeq instrument (Illumina) with paired end 78-per end sequencing. As a reference material in specific assays, we included RNA of known concentration and sequence (from bacteriophage MS2). From these data, we obtained partial information on strandedness and a set of segments that could be used for assembly. This was particularly useful for the Moderna vaccine, for which the original vaccine RNA sequence was not available at the time our study was carried out. Contigs encoding full-length spikes were assembled from the Moderna and Pfizer datasets. The Pfizer/BioNTech data [Figure 1] verified the reported sequence for that vaccine (https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/), while the Moderna sequence [Figure 2] could not be checked against a published reference. RNA preparations lacking dsRNA are desirable in generating vaccine formulations as these will minimize an otherwise dramatic biological (and nonspecific) response that vertebrates have to double stranded character in RNA (https://www.nature.com/articles/nrd.2017.243). In the sequence data that we analyzed, we found that the vast majority of reads were from the expected sense strand. In addition, the minority of antisense reads appeared different from sense reads in lacking the characteristic extensions expected from the template switching protocol. Examining only the reads with an evident template switch (as an indicator for strand-of-origin), we observed that both vaccines overwhelmingly yielded sense reads (>99.99%). Independent sequencing assays and other experimental measurements are ongoing and will be needed to determine whether this template-switched sense read fraction in the SmarterSeq protocol indeed represents the actual dsRNA content in the original material. This work provides an initial assessment of two RNAs that are now a part of the human ecosystem and that are likely to appear in numerous other high throughput RNA-seq studies in which a fraction of the individuals may have previously been vaccinated. ProtoAcknowledgements: Thanks to our colleagues for help and suggestions (Nimit Jain, Emily Greenwald, Lamia Wahba, William Wang, Amisha Kumar, Sameer Sundrani, David Lipman, Bijoyita Roy). Figure 1: Spike-encoding contig assembled from BioNTech/Pfizer BNT-162b2 vaccine. Although the full coding region is included, the nature of the methodology used for sequencing and assembly is such that the assembled contig could lack some sequence from the ends of the RNA. Within the assembled sequence, this hypothetical sequence shows a perfect match to the corresponding sequence from documents available online derived from manufacturer communications with the World Health Organization [as reported by https://berthub.eu/articles/posts/reverse-engineering-source-code-of-the-biontech-pfizer-vaccine/]. The 5’ end for the assembly matches the start site noted in these documents, while the read-based assembly lacks an interrupted polyA tail (A30(GCATATGACT)A70) that is expected to be present in the mRNA.

dkoslicki/Benchmarks

dkoslicki/CSrankings

A web app for ranking computer science departments according to their research output in selective venues, and for finding active faculty across a wide range of areas.

dkoslicki/d2s-documentation

Home for the Data2Services project documentation.

dkoslicki/dkoslicki.github.io

Koslicki Lab website

dkoslicki/EstimateUnknownAbundance

Using convex optimization to approximate the abundance of unknown organisms in metagenomic samples

dkoslicki/FITS_focus_data

Extract FWHM, temp, focus positions, etc. from a directory of astronomical FITS files

dkoslicki/IntroToMathSoftware

Course materials for MTH 321: Introduction to Mathematical Software, taught when I was at Oregon State University

dkoslicki/LinearRegressionMurderMystery

dkoslicki/MeTEA

(Metagenomic Taxa Evaluation and Assessment) Parse, organize, calculate and save data from metagenomic profile files

dkoslicki/MinimizeBiologicalDiversity

dkoslicki/mutation-rate-intervals

Support for "statistics of k-mers from a sequence undergoing a simple mutation process without spurious matches"

dkoslicki/phylogenetic-tree-using-fracminhash

dkoslicki/PlanetarySystemStacker

Produce a sharp image of a planetary system object (moon, sun, planets) from many seeing-affected frames according to the "lucky imaging" technique.

dkoslicki/sourmash

Quickly search, compare, and analyze genomic and metagenomic data sets.

dkoslicki/stable-diffusion

dkoslicki/TestRepo

To try out advanced merging

dkoslicki/translator-developer-documentation

Official comprehensive "mkdocs/readthedocs" templated developer documentation for the Translator community and

dkoslicki/TranslatorArchitecture