New search engine for single cell atlases

Date:
March 3, 2021
Source:
Wellcome Trust Sanger Institute
Summary:
A new software tool allows researchers to quickly query datasets
generated from single-cell sequencing. Users can identify which
cell types any combination of genes are active in. The open-access
'scfind' software enables swift analysis of multiple datasets
containing millions of cells by a wide range of users, on a
standard computer.



FULL STORY ==========================================================================
A new software tool allows researchers to quickly query datasets generated
from single-cell sequencing. Users can identify which cell types any combination of genes are active in. Published in Nature Methods on
1st March, the open-access 'scfind' software enables swift analysis of
multiple datasets containing millions of cells by a wide range of users,
on a standard computer.


========================================================================== Processing times for such datasets are just a few seconds, saving time
and computing costs. The tool, developed by researchers at the Wellcome
Sanger Institute, can be used much like a search engine, as users can
input free text as well as gene names.

Techniques to sequence the genetic material from an individual cell
have advanced rapidly over the last 10 years. Single-cell RNA sequencing (scRNAseq), used to assess which genes are active in individual cells,
can be used on millions of cells at once and generates vast amounts of
data (2.2 GB for the Human Kidney Atlas). Projects including the Human
Cell Atlas and the Malaria Cell Atlas are using such techniques to
uncover and characterise all of the cell types present in an organism
or population. Data must be easy to access and query, by a wide range
of researchers, to get the most value from them.

To allow for fast and efficient access, a new software tool called
scfind uses a two-step strategy to compress data ~100-fold. Efficient decompression makes it possible to quickly query the data. Developed by researchers at the Wellcome Sanger Institute, scfind can perform large
scale analysis of datasets involving millions of cells on a standard
computer without special hardware. Queries that used to take days to
return a result, now take seconds.

The new tool can also be used for analyses of multi-omics data, for
example by combining single-cell ATAC-seq data, which measures epigenetic activity, with scRNAseq data.

Dr Jimmy Lee, Postdoctoral Fellow at the Wellcome Sanger Institute,
and lead author of the research, said: "The advances of multiomics
methods have opened up an unprecedented opportunity to appreciate the
landscape and dynamics of gene regulatory networks. Scfind will help
us identify the genomic regions that regulate gene activity -- even if
those regions are distant from their targets." Scfind can also be used
to identify new genetic markers that are associated with, or define,
a cell type. The researchers show that scfind is a more accurate and
precise method to do this, compared with manually curated databases or
other computational methods available.

To make scfind more user friendly, it incorporates techniques from
natural language processing to allow for arbitrary queries.

Dr Martin Hemberg, former Group Leader at the Wellcome Sanger Institute,
now at Harvard Medical School and Brigham and Women's Hospital, said:
"Analysis of single-cell datasets usually requires basic programming
skills and expertise in genetics and genomics. To ensure that large
single-cell datasets can be accessed by a wide range of users, we
developed a tool that can function like a search engine -- allowing
users to input any query and find relevant cell types." Dr Jonah Cool,
Science Program Officer at the Chan Zuckerberg Initiative, said: "New,
faster analysis methods are crucial for finding promising insights
in single-cell data, including in the Human Cell Atlas. User-friendly
tools like scfind are accelerating the pace of science and the ability
of researchers to build off of each other's work, and the Chan Zuckerberg Initiative is proud to support the team that developed this technology." ========================================================================== Story Source: Materials provided by Wellcome_Trust_Sanger_Institute. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Jimmy Tsz Hang Lee, Nikolaos Patikas, Vladimir Yu Kiselev, Martin
Hemberg. Fast searches of large collections of single-cell data
using scfind. Nature Methods, 2021; DOI: 10.1038/s41592-021-01076-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/03/210303142439.htm

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