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      A 3D transcriptomics atlas of the mouse nose sheds light on the anatomical logic of smell

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          SUMMARY

          The sense of smell helps us navigate the environment, but its molecular architecture and underlying logic remain understudied. The spatial location of odorant receptor genes ( Olfrs) in the nose is thought to be independent of the structural diversity of the odorants they detect. Using spatial transcriptomics, we create a genome-wide 3D atlas of the mouse olfactory mucosa (OM). Topographic maps of genes differentially expressed in space reveal that both Olfrs and non- Olfrs are distributed in a continuous and overlapping fashion over at least five broad zones in the OM. The spatial locations of Olfrs correlate with the mucus solubility of the odorants they recognize, providing direct evidence for the chromatographic theory of olfaction. This resource resolves the molecular architecture of the mouse OM and will inform future studies on mechanisms underlying Olfr gene choice, axonal pathfinding, patterning of the nervous system, and basic logic for the peripheral representation of smell.

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          In brief

          Ruiz Tejada Segura et al. employ a spatial transcriptomics approach to create a 3D map of gene expression of the mouse nose and combine it with single-cell RNA-seq, machine learning, and chemoinformatics to resolve its molecular architecture and shed light into the anatomical logic of smell.

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          Most cited references161

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                25 March 2022
                22 March 2022
                11 April 2022
                : 38
                : 12
                : 110547
                Affiliations
                [1 ]Institute of Epigenetics and Stem Cells, Helmholtz Zentrum München, Feodor-Lynen-Strasse 21, 81377 München, Germany
                [2 ]Institute of Functional Epigenetics, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
                [3 ]Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
                [4 ]Sidra Medicine, P.O. Box 26999, Doha, Qatar
                [5 ]Physics Department, University of Turin and INFN, Via P. Giuria 1, 10125 Turin, Italy
                [6 ]Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
                [7 ]Department of Biochemistry, University of São Paulo, São Paulo, Brazil
                [8 ]Monell Chemical Senses Center, 3500 Market Street, Philadelphia, PA 19104, USA
                [9 ]Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
                [10 ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
                [11 ]College of Health and Life Sciences, Hamad Bin Khalifa University, P.O. Box 34110, Doha, Qatar
                [12 ]These authors contributed equally
                [13 ]These authors contributed equally
                [14 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                M.L.R.T.S. analyzed data and wrote the initial version of the manuscript. E.A.M. performed the RNA-seq experiments and analyzed data. T.S.N., L.S.M., L.W., and S.L. performed experiments. M.M., S.S.Y.H., J.D.M., F.V., M.C., and M.O. analyzed data. E.G., J.R., D.W.L., and B.M. analyzed data and helped write the manuscript. A.S. and L.R.S. conceived and supervised the project, analyzed data, and wrote the final version of the manuscript.

                Article
                NIHMS1791868
                10.1016/j.celrep.2022.110547
                8995392
                35320714
                90842419-3998-48c5-a048-3b252ca78319

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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