@Article{Kovaka2019,
author={Kovaka, Sam
and Zimin, Aleksey V.
and Pertea, Geo M.
and Razaghi, Roham
and Salzberg, Steven L.
and Pertea, Mihaela},
title={Transcriptome assembly from long-read RNA-seq alignments with StringTie2},
journal={Genome Biology},
year={2019},
month={Dec},
day={16},
volume={20},
number={1},
pages={278},
abstract={RNA sequencing using the latest single-molecule sequencing instruments produces reads that are thousands of nucleotides long. The ability to assemble these long reads can greatly improve the sensitivity of long-read analyses. Here we present StringTie2, a reference-guided transcriptome assembler that works with both short and long reads. StringTie2 includes new methods to handle the high error rate of long reads and offers the ability to work with full-length super-reads assembled from short reads, which further improves the quality of short-read assemblies. StringTie2 is more accurate and faster and uses less memory than all comparable short-read and long-read analysis tools.},
issn={1474-760X},
doi={10.1186/s13059-019-1910-1},
url={https://doi.org/10.1186/s13059-019-1910-1}
}

﻿@Article{Li2018,
author={Li, Heng},
title={Minimap2: pairwise alignment for nucleotide sequences},
journal={Bioinformatics},
year={2018},
month={Sep},
day={15},
volume={34},
number={18},
pages={3094-3100},
abstract={Recent advances in sequencing technologies promise ultra-long reads of ∼100 kb in average, full-length mRNA or cDNA reads in high throughput and genomic contigs over 100 Mb in length. Existing alignment programs are unable or inefficient to process such data at scale, which presses for the development of new alignment algorithms.Minimap2 is a general-purpose alignment program to map DNA or long mRNA sequences against a large reference database. It works with accurate short reads of ≥100{\thinspace}bp in length, ≥1{\thinspace}kb genomic reads at error rate ∼15{\%}, full-length noisy Direct RNA or cDNA reads and assembly contigs or closely related full chromosomes of hundreds of megabases in length. Minimap2 does split-read alignment, employs concave gap cost for long insertions and deletions and introduces new heuristics to reduce spurious alignments. It is 3--4 times as fast as mainstream short-read mappers at comparable accuracy, and is ≥30 times faster than long-read genomic or cDNA mappers at higher accuracy, surpassing most aligners specialized in one type of alignment.https://github.com/lh3/minimap2Supplementary data are available at Bioinformatics online.},
issn={1367-4803},
doi={10.1093/bioinformatics/bty191},
url={https://doi.org/10.1093/bioinformatics/bty191}
}

﻿@Article{Pertea2016,
author={Pertea, Mihaela
and Kim, Daehwan
and Pertea, Geo M.
and Leek, Jeffrey T.
and Salzberg, Steven L.},
title={Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown},
journal={Nature Protocols},
year={2016},
month={Sep},
day={01},
volume={11},
number={9},
pages={1650-1667},
abstract={Pertea et al. describe a protocol to analyze RNA-seq data using HISAT, StringTie and Ballgown (the `new Tuxedo' package). The protocol can be used for assembly of transcripts, quantification of gene expression levels and differential expression analysis.},
issn={1750-2799},
doi={10.1038/nprot.2016.095},
url={https://doi.org/10.1038/nprot.2016.095}
}

﻿@Article{Sessegolo2019,
author={Sessegolo, Camille
and Cruaud, Corinne
and Da Silva, Corinne
and Cologne, Audric
and Dubarry, Marion
and Derrien, Thomas
and Lacroix, Vincent
and Aury, Jean-Marc},
title={Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules},
journal={Scientific Reports},
year={2019},
month={Oct},
day={17},
volume={9},
number={1},
pages={14908},
abstract={Our vision of DNA transcription and splicing has changed dramatically with the introduction of short-read sequencing. These high-throughput sequencing technologies promised to unravel the complexity of any transcriptome. Generally gene expression levels are well-captured using these technologies, but there are still remaining caveats due to the limited read length and the fact that RNA molecules had to be reverse transcribed before sequencing. Oxford Nanopore Technologies has recently launched a portable sequencer which offers the possibility of sequencing long reads and most importantly RNA molecules. Here we generated a full mouse transcriptome from brain and liver using the Oxford Nanopore device. As a comparison, we sequenced RNA (RNA-Seq) and cDNA (cDNA-Seq) molecules using both long and short reads technologies and tested the TeloPrime preparation kit, dedicated to the enrichment of full-length transcripts. Using spike-in data, we confirmed that expression levels are efficiently captured by cDNA-Seq using short reads. More importantly, Oxford Nanopore RNA-Seq tends to be more efficient, while cDNA-Seq appears to be more biased. We further show that the cDNA library preparation of the Nanopore protocol induces read truncation for transcripts containing internal runs of T's. This bias is marked for runs of at least 15{\thinspace}T's, but is already detectable for runs of at least 9{\thinspace}T's and therefore concerns more than 20{\%} of expressed transcripts in mouse brain and liver. Finally, we outline that bioinformatics challenges remain ahead for quantifying at the transcript level, especially when reads are not full-length. Accurate quantification of repeat-associated genes such as processed pseudogenes also remains difficult, and we show that current mapping protocols which map reads to the genome largely over-estimate their expression, at the expense of their parent gene.},
issn={2045-2322},
doi={10.1038/s41598-019-51470-9},
url={https://doi.org/10.1038/s41598-019-51470-9}
}

﻿@Article{Tasic2018,
author={Tasic, Bosiljka
and Yao, Zizhen
and Graybuck, Lucas T.
and Smith, Kimberly A.
and Nguyen, Thuc Nghi
and Bertagnolli, Darren
and Goldy, Jeff
and Garren, Emma
and Economo, Michael N.
and Viswanathan, Sarada
and Penn, Osnat
and Bakken, Trygve
and Menon, Vilas
and Miller, Jeremy
and Fong, Olivia
and Hirokawa, Karla E.
and Lathia, Kanan
and Rimorin, Christine
and Tieu, Michael
and Larsen, Rachael
and Casper, Tamara
and Barkan, Eliza
and Kroll, Matthew
and Parry, Sheana
and Shapovalova, Nadiya V.
and Hirschstein, Daniel
and Pendergraft, Julie
and Sullivan, Heather A.
and Kim, Tae Kyung
and Szafer, Aaron
and Dee, Nick
and Groblewski, Peter
and Wickersham, Ian
and Cetin, Ali
and Harris, Julie A.
and Levi, Boaz P.
and Sunkin, Susan M.
and Madisen, Linda
and Daigle, Tanya L.
and Looger, Loren
and Bernard, Amy
and Phillips, John
and Lein, Ed
and Hawrylycz, Michael
and Svoboda, Karel
and Jones, Allan R.
and Koch, Christof
and Zeng, Hongkui},
title={Shared and distinct transcriptomic cell types across neocortical areas},
journal={Nature},
year={2018},
month={Nov},
day={01},
volume={563},
number={7729},
pages={72-78},
abstract={The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA ($\gamma$-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.},
issn={1476-4687},
doi={10.1038/s41586-018-0654-5},
url={https://doi.org/10.1038/s41586-018-0654-5}
}


