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C-Circles, an ALT Plan for Telomere Restoration

February 18, 2022

The telomerase ribonucleoprotein complex is the primary means by which yeast cells maintain telomeres. However, it turns out that cells lacking functional telomerase have a backup plan to restore telomere length by “alternative lengthening of telomeres” (ALT). ALT employs recombination via extrachromosomal telomere elements called C-circles. In a process for which the reasons remain unclear, C-circles get paired with eroded telomeres at the nuclear pore complex on the nuclear membrane. This pairing requires the SAGA/TREX2 complex and, once paired, the recombination between C-circles and telomeres appears to be effected by Rad59p, the paralog of Rad52p.

Model of type II telomere recombination that relies on telomeric circles, from Aguilera et al.

This interesting model is described in a recent paper in The EMBO Journal, in which Aguilera et al. adapt a method developed in human cancer studies to detect ALT and C-circles in yeast. In humans, ~10% of cancers depend on ALT for unchecked growth. In yeast, cells with ALT were able to be detected as survivors among telomerase mutant (est2∆) cells.

As other types of extrachromosomal DNA circles were previously reported to associate with the nuclear pore complex, the authors addressed the possibility that C-circles bind the NPC and demonstrated it clearly. They also showed the circles interact with the SAGA/TREX2 complex, which favors telomere recombination.

The novel finding that ALT in yeast so closely mirrors that of some human cancer cells is a boon to study of these cancers. The ability to develop ALT inhibitors in yeast would provide a new set of potential anticancer therapies, making this an ideal model system.

Categories: Research Spotlight

Tags: cancer, cell aging, Saccharomyces cerevisiae, senescence, telomeres, yeast model for human disease

Memories of Deceptive Courtship

February 10, 2022

Yeast courtship provides an excellent model for how a simple organism manages to remember past events. Recent yeast studies reveal how this memory can involve prion-like proteins that build up in a cell and persist as a form of memory. In a recent online issue of Current Biology, Lau et al. show how Whi3p self-templates into large assemblies upon perception of “deceptive courtship,” i.e. when pheromone is perceived but no mating partner appears. The super-assembly state allows escape from the G1 arrest triggered by potential courtship, but also prevents any further response to pheromone. While this pheromone refractory state is stable for the remainder of this mother cell’s life, daughter cells do not inherit this state and are fully pheromone responsive.  

From Caudron and Barral, 2013

The organization of domains in Whi3p and the ability to self-template into large assemblies are features shared with prions. The lack of infectious heritability, though, distinguishes Whi3p as a mnemon rather than a prion. Fascinatingly, Lau et al. show that assembled Whi3p becomes heritable in cells with defective diffusion barriers, where the physical barriers at the bud neck normally restrict the super-assembled protein to the mother cell.

Whi3 behavior ±pheromone and ±diffusion barrier, from Lau et al., 2022

In studies of diffusion barrier disruption, the authors observed that the main super-assembly of Whi3p remains in the mother cell, while the daughter cells get “seeds” of Whi3mnem upon which to assemble further. They note that transmission of seeds is most prevalent in the first few divisions following escape from pheromone arrest, which suggests there is a limiting factor to this diffusion. They propose a model in which the assembled Whi3mnem “matures” to a form that no longer propagates seeds to daughter cells, even in the absence of a barrier.

Thus, remarkably, prion-like behavior appears to be one of the most rudimentary forms of memory, and may have implication for understanding cell memory in higher organisms.

Categories: Research Spotlight

Tags: mnemons, prion-like proteins, Saccharomyces cerevisiae, yeast cell memory, yeast mating

New ncRNA Represses Zinc Regulator

February 04, 2022

In a twist to an established story, the termination of noncoding RNAs by the NNS complex (NRD1 snoRNA termination complex) appears to be dependent on the phosphorylation of a regulatory component. In the latest issue of Nucleic Acids Research, Haidara et al. show how the NNS-complex component Sen1p acts to repress transcription of the zinc master regular ZAP1 when Sen1p is phosphorylated, which appears to happen in response to excess zinc.

The NNS complex had previously been shown to terminate transcription of PHO84 via antisense RNA. In this current paper, the authors identify the previously unannotated noncoding RNA ZRN1 as lying directly upstream of ZAP1 and, when transcribed without termination, repressing the downstream gene. Termination of ZRN1 transcription by dephosphophorylated Sen1p derepresses ZAP1 mRNA levels via removal of the interfering RNA. As evidence of this relationship, a Sen1p phospho-mimetic mutation (T1623E) results in stable repression of ZAP1 transcription.

Working model for Sen1p regulation of gene expression, from Haidara et al.

The model proposed is that zinc excess leads to phosphorylation of Sen1p by an unidentified kinase, which then causes the level of ZRN1 transcript to increase because termination is impaired. This in turn represses ZAP1 mRNA levels by interference, thereby repressing genes responsible for increased zinc uptake and storage.  

Interestingly, the same system (i.e. Sen1p as a component of the NNS complex) represses PHO84 expression via interference, and PHO84 encodes a low-affinity Zn transporter that also contributes to zinc homeostasis. Might RNA interference play an expanded regulatory role over what is currently known?

Categories: Research Spotlight

Tags: noncoding RNA, Saccharomyces cerevisiae, transcription, zinc homeostasis

New Understanding of ER Membrane Expansion

January 28, 2022

How lipid metabolism gets coordinated with membrane growth in response to environment has remained an unexplained phenomenon. A recent paper explains key components in how cells manage this response.

Organelle biogenesis typically requires synthesis of new membrane and thus depends on lipid metabolism. A genetic screen identifies the ER protein Ice2p as a regulator of lipid metabolism and a major factor for ER membrane biogenesis, from Papagiannidis et al.

Papagiannidis et al. demonstrate a key regulatory step via the transmembrane protein Ice2p. Ice2p affects negative regulation of Pah1p (called lipin in metazoa) by inhibiting the Nem1-Spo7 phosphatase complex, which would normally activate Pah1p by dephosphorylation. The study shows that Ice2p also plays a role in transcriptional regulation of lipid synthesis genes, thereby providing a key pivot in the cellular need to make more membrane in response to ER stress.

Categories: Paper of the Week

Tags: endoplasmic reticulum, lipid regulation, Membrane biogenesis, Saccharomyces cerevisiae

Predicted 3D Structures of Yeast Complexes

January 20, 2022

In an exciting new paper, Humphreys et al. describe the use of deep-learning-based algorithms to predict structures of not only single proteins, but assemblies of proteins. The team used rapid RoseTTAFold combined with the more accurate AlphaFold to build structural models for 106 previously unidentified protein assemblies and 806 complexes that had not been structurally characterized. The complexes have up to five subunits and are involved in numerous critical roles in cell biology.

Examples of predicted complexes from Humphreys et al.

Go look for your own proteins of interest at the ModelArchive and search in the Home page. Also find the link on the resources section of the SGD Interaction and Protein pages.

Categories: Announcements, Data updates, Paper of the Week

Tags: protein complex, Saccharomyces cerevisiae, yeast protein assembly

SGD Newsletter, Fall 2021

December 14, 2021

About this newsletter:
This is the Fall 2021 issue of the SGD newsletter. The goal of this newsletter is to inform our users about new features in SGD and to foster communication within the yeast community. You can view this newsletter as well as previous newsletters on our Community Wiki.

Contents

Protein Complex Page Updates

Complex3.png

SGD has made recent updates to our protein complex pages to improve clarity and ease of use. The new pages for each complex will have the same format as gene pages, with tabs across the top for each category of information, including a Summary page, a Gene Ontology page, and a Literature page. Just as we do for all of your favorite genes, Gene Ontology and Literature curation for complexes will be ongoing.

If you have any questions or feedback about the updates to our complex pages, please do not hesitate to contact us at any time.

Nomenclature Updates

SGD has long been the keeper of the official Saccharomyces cerevisiae gene nomenclature. Robert Mortimer handed over this responsibility to SGD in 1993 after maintaining the yeast genetic map and gene nomenclature for 30 years.

The accepted format for gene names in S. cerevisiae comprises three uppercase letters followed by a number. The letters typically signify a phrase (referred to as the “Name Description” in SGD) that provides information about a function, mutant phenotype, or process related to that gene, for example “ADE” for “ADEnine biosynthesis” or “CDC” for “Cell Division Cycle”. Gene names for many types of chromosomal features follow this basic format regardless of the type of feature named, whether an ORF, a tRNA, another type of non-coding RNA, an ARS, or a genetic locus. Some S. cerevisiae gene names that pre-date the current nomenclature standards do not conform to this format, such as MRLP38, RPL1A, and OM45.

A few historical gene names predate both the nomenclature standards and the database, and were less computer-friendly than more recent gene names, due to the presence of punctuation. SGD recently updated these gene names to be consistent with current standards and to be more software-friendly by removing punctuation. The old names for these four genes have been retained as aliases.

Legacy gene names

ORFOld gene nameNew gene name
YGL234WADE5,7ADE57
YER069WARG5,6ARG56
YBR208CDUR1,2DUR12
YIL154CIMP2′IMP21

New systematic nomenclature for yeast genes not in the reference genome

For many years, a widely adopted systematic nomenclature has existed for yeast protein-coding genes, or ORFs, as many yeast researchers call them. Readers of the last SGD newsletter will recall that, earlier this year, SGD adopted a new systematic nomenclature for the entire annotated complement of ncRNAs.

We have just put into place a new systematic nomenclature for S. cerevisiae genes that are not found in the reference genome of strain S288C (“non-reference” genes). This new systematic nomenclature is similar to, but distinct from, that used for ORFs and that used for ncRNAs. Non-reference genes are designated by a symbol consisting of three uppercase letters and a four-digit number, as follows: Y for “Yeast”, SC for “Saccharomyces cerevisiae”, and a four-digit number corresponding to the sequential order in which the gene was added to SGD. We currently have 55 of these genes in SGD, some of which are old favorites like MAL21/YSC0004 and MATA/YSC0046, while others are more recent additions like XDH1/YSC0051. Going forward, as evidence is published pointing to other S. cerevisiae genes not present in the S288C reference genome, they will be added to the annotation using the next sequential number available. We already have 15 more of these YSC0000 names reserved by researchers and awaiting publication.

If you have some non-reference genes for which these names would be appropriate, please let us know!

Would you like to see the shape of your protein?

Hog1 structure.png

SGD now contains links to AlphaFold in the Resources sections of the Summary, Protein, and Homology pages for every gene.

  • The links through SGD give quick access to EMBL’s European Bioinformatics Institute (EMBL-EBI), which offers a new, highly accurate tool for predicting protein structure with speed and clarity.
  • Given a peptide sequence for an uncharacterized protein, AlphaFold will model predicted domains and provide relative confidence levels for each portion of the prediction.
  • The predicted domains can then be compared to known protein structures (using a tool such as PDBeFold) to seek matches to characterized protein families.
  • Whether or not a family is identified, the comparison will yield clues to protein function to help design the next experiments.

DIOPT Orthologs and New Queries in YeastMine

DIOPT-logo-integrative trans.png

We recently replaced HomoloGene, Ensembl, TreeFam and PANTHER homology datasets in YeastMine with homology data from DIOPT (DRSC integrative ortholog prediction tool). DIOPT integrates orthology predictions from multiple sources, including HomoloGene, Ensembl, TreeFam, and PANTHER. Using the Gene->Non-fungal and S. cerevisiae Homologs pre-generated query, you can look for DIOPT homologs for a single or multiple yeast genes. The results table provides identifiers and standard names for the yeast and homologous genes, as well as organism and predictive score information. As with other YeastMine templates, results can be saved as lists and analyzed further.

Pre-generated queries for human homolog(s) of your favorite yeast gene and their corresponding disease associations remain largely unchanged. You can begin with your favorite human gene or disease keyword and retrieve the yeast counterparts of the relevant gene(s). As an example, you can search for the S. cerevisiae homologs of all human genes associated with disorders that contain the keyword “diabetes” (view search). The results table provides identifiers and standard names for the yeast and human genes, OMIM gene and disease identifiers and name, as well as predictive algorithm sources and scores.

Alliance of Genome Resources – Recent Release

alliance logo.png

The Alliance of Genome Resources, a collaborative effort from SGD and other model organism databases (MOD), released version 4.1 this past August. Notable improvements and new features include:

  • Human and model organism high throughput (HTP) variant data
    • Human variants are imported from Ensembl
    • Model organism HTP variants are submitted by Alliance members (FlyBase, RGD, SGD, Wormbase) or imported from EVA (MGI and ZFIN).
    • Added HTP variants to the Alleles and Variants table on gene pages (e.g. rat Lepr Gene page) and to the table on the Alleles and Variants Details page (e.g. rat Lepr Alleles and Variants Details.
    • Created a report page for Human and model organism HTP variants (e.g. human variant rs1041354454).
    • Expanded Allele Category in search to “Allele/Variant” and added a search for HTP variants.
  • On Gene Pages, a new Pathways widget displays via tabs:
    • Reactome models of pathways for human gene products as well as inferred pathways for model organism genes based on orthology to human genes.
    • Reactome reactions for gene products (e.g. human TP53 Gene page)
    • Gene Ontology Causal Activity Models (GO-CAMs). These provide a framework to represent a biological system by linking together multiple GO annotations. PMID:31548717 (e.g. worm nsy-1 Gene page).
  • Experimental conditions are include for Disease and Phenotype data in tables on Gene, Allele, and Disease pages (e.g. zebrafish scn1lab Gene page).
  • AllianceMine added Orthologs, and Allele and Variants (low throughput) data types to this release. You can now query for these data types via pre-made template queries.
  • The Alliance Community Forum is released. The Forum permits discussions across six model organism communities—flies, mice, yeast, rats, worms, and zebrafish. More details will follow.

Upcoming Conferences and Courses

  • Fungal Genetics – the premier meeting for the international community of fungal geneticists
    • Asilomar Conference Grounds, Pacific Grove, California (and Online)
    • March 15 – 20, 2022
  • 36th International Specialised Symposium on Yeasts (ISSY36) – Yeast Sea to Sky – Yeast in the Genomics Era
    • University of British Columbia, Vancouver
    • July 12 – 16, 2022
  • CSHL Yeast Genetics & Genomics – modern, intensive laboratory course that teaches students full repertoire of genetic and genomic approaches
    • Cold Spring Harbor Laboratory, NY
    • July 26 – August 15, 2022
  • Yeast Genetics Meeting – the premier meeting for students, postdoctoral scholars, research staff, and principal investigators studying various aspects of eukaryotic biology in yeast
    • University of California, Los Angeles
    • August 17 – 21, 2022

Gene Ontology Consortium Fall 2021 Meeting

logo GOC.png

From October 12-14, SGD biocurators attended the Gene Ontology Consortium’s Fall Meeting with participants from around the world. The goal of these meetings is to bring together data scientists with diverse backgrounds (curators, programmers, etc.) for lively discussions regarding how to better capture, curate, analyze, and serve data to researchers, educators, students, and other life science professionals. Our goal in participating in these meetings each year is to find ways to make SGD even better for you!

Discussion topics included, but were not limited to:

  • LitSuggest – web-based system for biomedical literature recommendation and curation
  • ECO, Evidence and Conclusions Ontology – terms used to describe types of evidence and assertion methods
  • PAINT, Phylogenetic Annotation and INference Tool from PANTHER – orthology between reference genome genes and human disease genes

Happy Holidays from SGD!

SnowShmoo.png

We know that 2021 has been another challenging year for everyone. Our thoughts go out to all those who have been impacted by recent events. We wish you and your family, friends, and lab mates the best during the upcoming holidays.

Stanford University will be closed for two weeks starting December 20, and will reopen on January 3rd, 2022. Although SGD staff members will be taking time off, the website will be up and running throughout the winter break, and we will resume responding to user requests and questions in the new year.

Categories: Newsletter

Tags: Newsletter, Saccharomyces cerevisiae

Protein Complex Page Updates

December 01, 2021

SGD has updated our protein complex pages to have the same format as gene pages, with tabs across the top for each category of information, including a Summary page, a new Gene Ontology page, and a new Literature page for each complex. Just as we do for all of your favorite genes, Gene Ontology and Literature curation for complexes will be ongoing.

Summary page and new Literature page

If you have any questions or feedback about the updates to our complex pages, please do not hesitate to contact us at any time.

Categories: Announcements, Data updates, Website changes

Tags: protein complex, Saccharomyces cerevisiae

New links to AlphaFold 3D Predicted Protein Structure Database

November 09, 2021

  • The links through SGD give quick access to EMBL–European Bioinformatics Institute‘s new, highly accurate tool for predicting protein structure.
  • Given a peptide sequence for an uncharacterized protein, AlphaFold will model predicted domains and provide relative confidence levels for each portion of the prediction.
  • The predicted domains can then be compared to known protein structures (using a tool such as PDBeFold to seek matches to characterized protein families).
  • Whether or not a family is identified, the comparison will yield clues to protein function to help design the next experiments.
Structure of Hog1p

Categories: Data updates

Tags: AlphaFold, new tools

Updates to legacy gene names

November 05, 2021

SGD has long been the keeper of the official Saccharomyces cerevisiae gene nomenclature. Robert Mortimer handed over this responsibility to SGD in 1993 after maintaining the yeast genetic map and gene nomenclature for 30 years. 

The accepted format for gene names in S. cerevisiae comprises three uppercase letters followed by a number. The letters typically signify a phrase (referred to as the “Name Description” in SGD) that provides information about a function, mutant phenotype, or process related to that gene, for example “ADE” for “ADEnine biosynthesis” or “CDC” for “Cell Division Cycle”. Gene names for many types of chromosomal features follow this basic format regardless of the type of feature named, whether an ORF, a tRNA, another type of non-coding RNA, an ARS, or a genetic locus. Some S. cerevisiae gene names that pre-date the current nomenclature standards do not conform to this format, such as MRLP38, RPL1A, and OM45. 

A few historical gene names predate both the nomenclature standards and the database, and were less computer-friendly than more recent gene names, due to the presence of punctuation. SGD recently updated these gene names to be consistent with current standards and to be more software-friendly by removing punctuation. The old names for these four genes have been retained as aliases.

ORFOld gene nameNew gene name
YGL234WADE5,7ADE57
YER069WARG5,6ARG56
YBR208CDUR1,2DUR12
YIL154CIMP2′IMP21

Categories: Announcements, Data updates

Tags: gene nomenclature

Reference Genome Annotation Update R64.3

August 03, 2021

The S. cerevisiae strain S288C reference genome annotation was updated in its first major update since 2014. The new genome annotation is release R64.3, which released on April 21, 2021. Note that the underlying sequence of 16 assembled nuclear chromosomes, plus the mitochondrial genome, remained unchanged in annotation release R64.3.1 (relative to genome sequence release R64.2.1).

This annotation update included:

R64.3 Annotation update details

ChrFeatureDescription of changeReference
IIYNCB0008W aka GAL10-ncRNANew ncRNA antisense to GAL10coordinates 276805..280645Houseley et al 2008 PMID:19061643,Pinskaya et al 2009 PMID:19407817,
Geisler et al 2012 PMID:22226051
IIYNCB0014W aka TBRT/XUT_2F-154New ncRNA antisense to TAT1coordinates 376610..378633Awasthi et al 2020 PMID:32081726
IIIRE/RE301New recombination enhancercoordinates 29108..29809Wu and Haber 1996 PMID:8861911
VYELWdelta27New Ty1 LTRcoordinates 449274..449626Nene et al 2018 PMID:29320491
VHPA3/YEL066WMoved translation start to Met19old coordinates: 26667..27206new coordinates: 26721..27206Sampath et al 2013 PMID:23775086
VIIOTO1/YGR227C-ANew ORFcoordinates 949052..949225 CrickMakanae et al 2015 PMID:25781884
VIIROK1/YGL171WTwo new uORFscoordinates uORF1: 182286..182407coordinates uORF2: 182291..182329Jeon and Kim 2010 PMID:20969870
VIIIYHR052C-BNew ORFcoordinates: 212519..212692 CrickHe et al 2018 PMID:29897761
VIIIYHR054C-BNew ORFcoordinates: 214517..214690 CrickHe et al 2018 PMID:29897761
VIIISUT169/YNCH0011WNew ncRNAcoordinates 378254..379237Xu et al 2009 PMID:19169243,Geisler et al 2012 PMID:22226051,
Huber et al 2016 PMID:27292640,
Bunina et al 2017 PMID:28977638
XYJR012CMoved start to Met76old coordinates: 459795..460418 Cricknew coordinates: 459795..460193 CrickSadhu et al 2018 PMID:29632376
XYJR107C-ANew ORFcoordinates 628457..628693 CrickYagoub et al 2015 PMID:26554900,
He et al 2018 PMID:29897761
XIYKL104W-ANew ORFcoordinates 245032..245286He et al 2018 PMID:29897761
XIIYLR379W-ANew ORFcoordinates: 877444..877716Internal reanalysis of results from Song et al 2015 to find and annotate missing S288C ORFs PMID:25781462
XIINTS1-2, NTS2-1, NTS2-2Change feature_type/SO_term from SO:0001637 rRNA_gene to SO:0000183 non_transcribed_region
XIIILDO45/YMR147WShift stop to be same as LDO16/YMR148W, add intronold coordinates 559199..559870new coordinates 559199..559780, 560156..560812Eisenberg-Bord et al 2018 PMID:29187527
XIIIYMR008C-ANew ORFcoordinates 283081..283548 CrickInternal reanalysis of results from Song et al 2015 to find and annotate missing S288C ORFs PMID:25781462
XIIIYNCM0001W aka PHO84 lncRNANew ncRNAcoordinates: 23564..26578Camblong et al 2007 PMID:18022365
XIVLTO1/YNL260CMove start to Met37old coordinates: 156859..157455 Cricknew coordinates: 156859..157347 CrickPaul et al 2015 PMID:26182403
XVIYNCP0002W aka GAL4 lncRNANew ncRNAcoordinates: 79562..82648Geisler et al 2012 PMID:22226051

New systematic nomenclature system for noncoding RNA genes

ChrSystematic_nameGene_nameAliasFeature_typeCoordinatesStrand
IYNCA0001WHRA1ncRNA gene99305..99868+
IYNCA0002WTRN1tP(UGG)A, tRNA-ProtRNA gene139152..139254+
IYNCA0003WSNR18snR18snoRNA gene142367..142468+
IYNCA0004WTGA1tA(UGC)A, tRNA-AlatRNA gene166267..166339+
IYNCA0005WSUP56tL(CAA)A, tRNA-LeutRNA gene181141..181254+
IYNCA0006CtS(AGA)A, tRNA-SertRNA gene182522..182603–
IIYNCB0001WtL(UAA)B1, tRNA-LeutRNA gene9583..9666+
IIYNCB0002WtF(GAA)B, tRNA-PhetRNA gene36398..36488+
IIYNCB0003WSNR56snR56snoRNA gene88190..88277+
IIYNCB0004WtI(AAU)B, tRNA-IletRNA gene197494..197567+
IIYNCB0005CtG(GCC)B, tRNA-GlytRNA gene197629..197699–
IIYNCB0006WtS(AGA)B, tRNA-SertRNA gene227075..227156+
IIYNCB0007WtT(AGU)B, tRNA-ThrtRNA gene266378..266450+
IIYNCB0008WGAL10-ncRNAncRNA gene276805..280645+
IIYNCB0009CSNR161snR161snoRNA gene307185..307345–
IIYNCB0010WTLC1telomerase RNA gene307587..308887+
IIYNCB0011CtV(UAC)B, tRNA-ValtRNA gene326792..326865–
IIYNCB0012WtL(UAA)B2, tRNA-LeutRNA gene347603..347686+
IIYNCB0013WtQ(UUG)B, tRNA-GlntRNA gene350827..350898+
IIYNCB0014WTBRTXUT_2F-154ncRNA gene376610..378633+
IIYNCB0015WtR(UCU)B, tRNA-ArgtRNA gene405878..405949+
IIYNCB0016WtD(GUC)B, tRNA-AsptRNA gene405960..406031+
IIYNCB0017CtC(GCA)B, tRNA-CystRNA gene643007..643078–
IIYNCB0018WtE(UUC)B, tRNA-GlutRNA gene645167..645238+
IIYNCB0019CLSR1snRNA gene680688..681862–
IIIYNCC0001CtE(UUC)C, tRNA-GlutRNA gene82462..82533–
IIIYNCC0002WSUP53tL(CAA)C, tRNA-LeutRNA gene90859..90972+
IIIYNCC0003CSNR43snR43snoRNA gene107504..107712–
IIIYNCC0004CSUF2tP(AGG)C, tRNA-ProtRNA gene123577..123648–
IIIYNCC0005WtN(GUU)C, tRNA-AsntRNA gene127716..127789+
IIIYNCC0006CSNR33snR33snoRNA gene142364..142546–
IIIYNCC0007CSUF16tG(GCC)C, tRNA-GlytRNA gene142701..142771–
IIIYNCC0008WtM(CAU)C, tRNA-MettRNA gene149920..149991+
IIIYNCC0009CtK(CUU)C, tRNA-LystRNA gene151284..151356–
IIIYNCC0010CtQ(UUG)C, tRNA-GlntRNA gene168301..168372–
IIIYNCC0011WSNR65snR65snoRNA gene177183..177282+
IIIYNCC0012CSNR189snR189snoRNA gene178610..178798–
IIIYNCC0013WSUP61tS(CGA)C, tRNA-SertRNA gene227942..228042+
IIIYNCC0014WtT(AGU)C, tRNA-ThrtRNA gene295484..295556+
IVYNCD0001WtG(GCC)D1, tRNA-GlytRNA gene83548..83618+
IVYNCD0002CSNR63snR63snoRNA gene323217..323471–
IVYNCD0003WtK(UUU)D, tRNA-LystRNA gene359577..359672+
IVYNCD0004WtA(AGC)D, tRNA-AlatRNA gene410379..410451+
IVYNCD0005CtT(AGU)D, tRNA-ThrtRNA gene434264..434336–
IVYNCD0006WtS(AGA)D1, tRNA-SertRNA gene437772..437853+
IVYNCD0007CtV(UAC)D, tRNA-ValtRNA gene488797..488870–
IVYNCD0008WtL(UAA)D, tRNA-LeutRNA gene519743..519826+
IVYNCD0009WtQ(UUG)D1, tRNA-GlntRNA gene520972..521043+
IVYNCD0010CSNR47snR47snoRNA gene541602..541700–
IVYNCD0011WtR(UCU)D, tRNA-ArgtRNA gene568882..568953+
IVYNCD0012WtD(GUC)D, tRNA-AsptRNA gene568964..569035+
IVYNCD0013CtR(ACG)D, tRNA-ArgtRNA gene619969..620041–
IVYNCD0014CtQ(UUG)D2, tRNA-GlntRNA gene645153..645224–
IVYNCD0015CtI(AAU)D, tRNA-IletRNA gene668007..668080–
IVYNCD0016CtQ(UUG)D3, tRNA-GlntRNA gene802731..802802–
IVYNCD0017WtI(UAU)D, tRNA-IletRNA gene884361..884493+
IVYNCD0018WSUP2tY(GUA)D, tRNA-TyrtRNA gene946312..946400+
IVYNCD0019CtS(AGA)D2, tRNA-SertRNA gene980974..981055–
IVYNCD0020WtG(GCC)D2, tRNA-GlytRNA gene992832..992902+
IVYNCD0021CtE(CUC)D, tRNA-GlutRNA gene1017207..1017278–
IVYNCD0022CtV(CAC)D, tRNA-ValtRNA gene1075472..1075544–
IVYNCD0023CtF(GAA)D, tRNA-PhetRNA gene1095370..1095461–
IVYNCD0024CtX(XXX)DtRNA gene1150842..1150941–
IVYNCD0025WEMT1tM(CAU)D, tRNA-MettRNA gene1175829..1175901+
IVYNCD0026WtK(CUU)D1, tRNA-LystRNA gene1201750..1201822+
IVYNCD0027CSUF3tG(CCC)D, tRNA-GlytRNA gene1257008..1257079–
IVYNCD0028WtS(AGA)D3, tRNA-SertRNA gene1305630..1305712+
IVYNCD0029CtK(CUU)D2, tRNA-LystRNA gene1352466..1352538–
IVYNCD0030WSNR13snR13snoRNA gene1402919..1403042+
IVYNCD0031CtL(CAA)D, tRNA-LeutRNA gene1461715..1461829–
IVYNCD0032CSNR84snR84snoRNA gene1492477..1493026–
VYNCE0001CSNR80snR80snoRNA gene52150..52320–
VYNCE0002WSNR67snR67snoRNA gene61352..61433+
VYNCE0003WSNR53snR53snoRNA gene61699..61789+
VYNCE0004CtG(GCC)E, tRNA-GlytRNA gene61890..61960–
VYNCE0005WtS(AGA)E, tRNA-SertRNA gene86604..86685+
VYNCE0006WIMT4tM(CAU)E, tRNA-MettRNA gene100133..100204+
VYNCE0007CRPR1ncRNA gene117667..118035–
VYNCE0008CtQ(UUG)E2, tRNA-GlntRNA gene131082..131153–
VYNCE0009CtK(CUU)E1, tRNA-LystRNA gene135425..135497–
VYNCE0010WtR(UCU)E, tRNA-ArgtRNA gene138666..138737+
VYNCE0011CSNR14snR14snRNA gene167427..167586–
VYNCE0012WtE(UUC)E1, tRNA-GlutRNA gene177099..177170+
VYNCE0013WtH(GUG)E1, tRNA-HistRNA gene207357..207428+
VYNCE0014WtQ(UUG)E1, tRNA-GlntRNA gene250286..250357+
VYNCE0015CSUP19tS(UGA)E, tRNA-SertRNA gene288443..288524–
VYNCE0016CtA(UGC)E, tRNA-AlatRNA gene312023..312095–
VYNCE0017WSRG1ncRNA gene322212..322762+
VYNCE0018WtE(UUC)E2, tRNA-GlutRNA gene354934..355005+
VYNCE0019WSNR4snR4snoRNA gene424698..424883+
VYNCE0020CSNR52snR52snoRNA gene431129..431220–
VYNCE0021CtH(GUG)E2, tRNA-HistRNA gene434541..434612–
VYNCE0022CtK(CUU)E2, tRNA-LystRNA gene435752..435824–
VYNCE0023WtV(AAC)E1, tRNA-ValtRNA gene438700..438773+
VYNCE0024WSCR1ncRNA gene441987..442508+
VYNCE0025CtI(AAU)E1, tRNA-IletRNA gene443202..443275–
VYNCE0026WtV(AAC)E2, tRNA-ValtRNA gene469457..469530+
VYNCE0027CtE(UUC)E3, tRNA-GlutRNA gene487331..487402–
VYNCE0028CtR(ACG)E, tRNA-ArgtRNA gene492352..492424–
VYNCE0029CtI(AAU)E2, tRNA-IletRNA gene551285..551358–
VIYNCF0001CRUF21ncRNA gene57815..58521–
VIYNCF0002WSUF9tP(UGG)F, tRNA-ProtRNA gene101376..101478+
VIYNCF0003CRUF20ncRNA gene131061..131503–
VIYNCF0004CtN(GUU)F, tRNA-AsntRNA gene137486..137559–
VIYNCF0005CtF(GAA)F, tRNA-PhetRNA gene157916..158007–
VIYNCF0006WSUF20tG(GCC)F1, tRNA-GlytRNA gene162228..162298+
VIYNCF0007WSUP11tY(GUA)F1, tRNA-TyrtRNA gene167437..167525+
VIYNCF0008CtG(GCC)F2, tRNA-GlytRNA gene180974..181044–
VIYNCF0009CtS(GCU)F, tRNA-SertRNA gene191513..191613–
VIYNCF0010CRUF22ncRNA gene199299..199813–
VIYNCF0011CtA(AGC)F, tRNA-AlatRNA gene204924..204996–
VIYNCF0012CSUP6tY(GUA)F2, tRNA-TyrtRNA gene210619..210707–
VIYNCF0013WRUF23ncRNA gene221714..221967+
VIYNCF0014CtK(CUU)F, tRNA-LystRNA gene226688..226760–
VIIYNCG0001CRME3ncRNA gene33109..35013–
VIIYNCG0002CtV(AAC)G3, tRNA-ValtRNA gene73829..73902–
VIIYNCG0003CtH(GUG)G1, tRNA-HistRNA gene110625..110696–
VIIYNCG0004WtK(UUU)G1, tRNA-LystRNA gene115488..115583+
VIIYNCG0005WtK(CUU)G1, tRNA-LystRNA gene122269..122341+
VIIYNCG0006CRME2ncRNA gene141898..144120–
VIIYNCG0007CtK(CUU)G2, tRNA-LystRNA gene185714..185786–
VIIYNCG0008WtL(CAA)G1, tRNA-LeutRNA gene205521..205634+
VIIYNCG0009CtW(CCA)G1, tRNA-TrptRNA gene287350..287455–
VIIYNCG0010WSNR82snR82snoRNA gene316788..317055+
VIIYNCG0011WtH(GUG)G2, tRNA-HistRNA gene319781..319852+
VIIYNCG0012WSOE1tE(UUC)G1, tRNA-GlutRNA gene328583..328654+
VIIYNCG0013WSNR10snR10snoRNA gene345986..346230+
VIIYNCG0014CSNR39snR39snoRNA gene365163..365251–
VIIYNCG0015CSNR39BsnR39BsnoRNA gene366374..366469–
VIIYNCG0016CtE(UUC)G2, tRNA-GlutRNA gene401527..401598–
VIIYNCG0017WtR(UCU)G1, tRNA-ArgtRNA gene405470..405541+
VIIYNCG0018CtV(AAC)G1, tRNA-ValtRNA gene412294..412367–
VIIYNCG0019WSUP54tL(CAA)G2, tRNA-LeutRNA gene423092..423205+
VIIYNCG0020CtF(GAA)G, tRNA-PhetRNA gene440716..440807–
VIIYNCG0021WtD(GUC)G1, tRNA-AsptRNA gene531610..531681+
VIIYNCG0022WtE(UUC)G3, tRNA-GlutRNA gene541850..541921+
VIIYNCG0023CtD(GUC)G2, tRNA-AsptRNA gene544577..544648–
VIIYNCG0024WSNR46snR46snoRNA gene545370..545566+
VIIYNCG0025CtS(AGA)G, tRNA-SertRNA gene561662..561743–
VIIYNCG0026WSNR48snR48snoRNA gene609584..609696+
VIIYNCG0027WtT(UGU)G1, tRNA-ThrtRNA gene661749..661820+
VIIYNCG0028WtL(GAG)G, tRNA-LeutRNA gene700675..700756+
VIIYNCG0029CtK(UUU)G2, tRNA-LystRNA gene700953..701048–
VIIYNCG0030CtC(GCA)G, tRNA-CystRNA gene707108..707179–
VIIYNCG0031WtN(GUU)G, tRNA-AsntRNA gene731137..731210+
VIIYNCG0032WtR(UCU)G3, tRNA-ArgtRNA gene736340..736411+
VIIYNCG0033WtI(AAU)G, tRNA-IletRNA gene739122..739195+
VIIYNCG0034WtA(AGC)G, tRNA-AlatRNA gene774349..774421+
VIIYNCG0035WSUF4tG(UCC)G, tRNA-GlytRNA gene779616..779687+
VIIYNCG0036WtA(UGC)G, tRNA-AlatRNA gene794417..794489+
VIIYNCG0037WtV(AAC)G2, tRNA-ValtRNA gene823482..823555+
VIIYNCG0038CtR(UCU)G2, tRNA-ArgtRNA gene828723..828794–
VIIYNCG0039WtG(GCC)G1, tRNA-GlytRNA gene845649..845719+
VIIYNCG0040CtL(CAA)G3, tRNA-LeutRNA gene857378..857491–
VIIYNCG0041WtK(CUU)G3, tRNA-LystRNA gene876394..876466+
VIIYNCG0042CtW(CCA)G2, tRNA-TrptRNA gene878710..878815–
VIIYNCG0043CtG(GCC)G2, tRNA-GlytRNA gene930953..931023–
VIIYNCG0044CSNR7-LsnR7-LsnRNA gene939459..939672–
VIIYNCG0045CSNR7-SsnR7-SsnRNA gene939494..939672–
VIIYNCG0046WtT(UGU)G2, tRNA-ThrtRNA gene1004216..1004287+
VIIIYNCH0001WtH(GUG)H, tRNA-HistRNA gene62755..62826+
VIIIYNCH0002CtV(AAC)H, tRNA-ValtRNA gene85298..85371–
VIIIYNCH0003CtT(AGU)H, tRNA-ThrtRNA gene116107..116179–
VIIIYNCH0004CtS(AGA)H, tRNA-SertRNA gene133026..133107–
VIIIYNCH0005WtQ(UUG)H, tRNA-GlntRNA gene134321..134392+
VIIIYNCH0006CtA(AGC)H, tRNA-AlatRNA gene146242..146314–
VIIIYNCH0007WRUF5-1ncRNA gene212409..213118+
VIIIYNCH0008WRUF5-2ncRNA gene214407..215116+
VIIIYNCH0009CtF(GAA)H1, tRNA-PhetRNA gene237848..237939–
VIIIYNCH0010CtF(GAA)H2, tRNA-PhetRNA gene358478..358569–
VIIIYNCH0011WSUT169ncRNA gene378254..379237+
VIIIYNCH0012WSNR32snR32snoRNA gene381540..381727+
VIIIYNCH0013CSUF8tP(UGG)H, tRNA-ProtRNA gene388893..388995–
VIIIYNCH0014WSNR71snR71snoRNA gene411228..411317+
VIIIYNCH0015WtT(UGU)H, tRNA-ThrtRNA gene466990..467061+
VIIIYNCH0016CtV(CAC)H, tRNA-ValtRNA gene475706..475778–
IXYNCI0001WSNR68snR68snoRNA gene97111..97246+
IXYNCI0002WtT(AGU)I1, tRNA-ThrtRNA gene175031..175103+
IXYNCI0003CtI(AAU)I1, tRNA-IletRNA gene183440..183513–
IXYNCI0004WtE(CUC)I, tRNA-GlutRNA gene197592..197663+
IXYNCI0005WtI(AAU)I2, tRNA-IletRNA gene210665..210738+
IXYNCI0006WSUP17tS(UGA)I, tRNA-SertRNA gene248850..248931+
IXYNCI0007CtK(CUU)I, tRNA-LystRNA gene300228..300300–
IXYNCI0008CtD(GUC)I1, tRNA-AsptRNA gene324303..324374–
IXYNCI0009WtT(AGU)I2, tRNA-ThrtRNA gene325748..325820+
IXYNCI0010CtD(GUC)I2, tRNA-AsptRNA gene336349..336420–
IXYNCI0011WtE(UUC)I, tRNA-GlutRNA gene370417..370488+
IXYNCI0012ICR1ncRNA gene393884..397082–
IXYNCI0013WPWR1ncRNA gene395999..396939+
XYNCJ0001CtT(AGU)J, tRNA-ThrtRNA gene59100..59172–
XYNCJ0002CtE(UUC)J, tRNA-GlutRNA gene115939..116010–
XYNCJ0003CSNR128snR128snoRNA gene139566..139691–
XYNCJ0004CSNR190snR190snoRNA gene139761..139950–
XYNCJ0005CtA(AGC)J, tRNA-AlatRNA gene197313..197385–
XYNCJ0006WtD(GUC)J1, tRNA-AsptRNA gene204735..204806+
XYNCJ0007CSNR37snR37snoRNA gene228094..228479–
XYNCJ0008WtR(ACG)J, tRNA-ArgtRNA gene233939..234011+
XYNCJ0009CSNR60snR60snoRNA gene349130..349233–
XYNCJ0010WSUP7tY(GUA)J1, tRNA-TyrtRNA gene354244..354332+
XYNCJ0011WtR(UCU)J1, tRNA-ArgtRNA gene355374..355445+
XYNCJ0012WtD(GUC)J2, tRNA-AsptRNA gene355456..355527+
XYNCJ0013CtD(GUC)J3, tRNA-AsptRNA gene374424..374495–
XYNCJ0014CtR(UCU)J2, tRNA-ArgtRNA gene374506..374577–
XYNCJ0015WtV(AAC)J, tRNA-ValtRNA gene378360..378433+
XYNCJ0016CEMT5tM(CAU)J1, tRNA-MettRNA gene391043..391115–
XYNCJ0017CtG(GCC)J1, tRNA-GlytRNA gene396726..396796–
XYNCJ0018WtK(CUU)J, tRNA-LystRNA gene414966..415038+
XYNCJ0019CtW(CCA)J, tRNA-TrptRNA gene415931..416036–
XYNCJ0020WEMT3tM(CAU)J2, tRNA-MettRNA gene422937..423009+
XYNCJ0021CSUP51tL(UAA)J, tRNA-LeutRNA gene424432..424515–
XYNCJ0022CIMT3tM(CAU)J3, tRNA-MettRNA gene517813..517884–
XYNCJ0023CtS(AGA)J, tRNA-SertRNA gene524012..524093–
XYNCJ0024WSUF23tG(GCC)J2, tRNA-GlytRNA gene531828..531898+
XYNCJ0025WHSX1tR(CCU)J, tRNA-ArgtRNA gene538555..538626+
XYNCJ0026WtD(GUC)J4, tRNA-AsptRNA gene541508..541579+
XYNCJ0027CSUP4tY(GUA)J2, tRNA-TyrtRNA gene542956..543044–
XYNCJ0028CIRT1ncRNA gene605936..607424–
XYNCJ0029WtL(UAG)J, tRNA-LeutRNA gene617919..618019+
XYNCJ0030WSNR3snR3snoRNA gene663749..663942+
XIYNCK0001WSNR64snR64snoRNA gene38811..38911+
XIYNCK0002CTRT2tT(CGU)K, tRNA-ThrtRNA gene46735..46806–
XIYNCK0003WtN(GUU)K, tRNA-AsntRNA gene74624..74697+
XIYNCK0004WtL(UAA)K, tRNA-LeutRNA gene84208..84291+
XIYNCK0005CtE(UUC)K, tRNA-GlutRNA gene141018..141089–
XIYNCK0006CtR(UCU)K, tRNA-ArgtRNA gene162487..162558–
XIYNCK0007WtK(CUU)K, tRNA-LystRNA gene202999..203071+
XIYNCK0008WtA(AGC)K1, tRNA-AlatRNA gene219895..219967+
XIYNCK0009WSNR38snR38snoRNA gene283185..283279+
XIYNCK0010WtW(CCA)K, tRNA-TrptRNA gene302918..303023+
XIYNCK0011CtV(AAC)K1, tRNA-ValtRNA gene308144..308217–
XIYNCK0012CtH(GUG)K, tRNA-HistRNA gene313401..313472–
XIYNCK0013WSNR69snR69snoRNA gene364776..364876+
XIYNCK0014WtV(AAC)K2, tRNA-ValtRNA gene379680..379753+
XIYNCK0015CSNR87snR87snoRNA gene431030..431138–
XIYNCK0016WtL(CAA)K, tRNA-LeutRNA gene458557..458670+
XIYNCK0017WtR(ACG)K, tRNA-ArgtRNA gene490968..491040+
XIYNCK0018CtD(GUC)K, tRNA-AsptRNA gene513332..513403–
XIYNCK0019WtA(AGC)K2, tRNA-AlatRNA gene517988..518060+
XIYNCK0020CSNR42snR42snoRNA gene559016..559366–
XIYNCK0021WtK(UUU)K, tRNA-LystRNA gene578965..579060+
XIIYNCL0001WtP(UGG)L, tRNA-ProtRNA gene92548..92650+
XIIYNCL0002CtS(AGA)L, tRNA-SertRNA gene167944..168025–
XIIYNCL0003WSNR30snR30snoRNA gene198784..199389+
XIIYNCL0004CtA(UGC)L, tRNA-AlatRNA gene214883..214955–
XIIYNCL0005CSNR79snR79snoRNA gene348427..348510–
XIIYNCL0006WSNR6snR6snRNA gene366235..366346+
XIIYNCL0007WtR(ACG)L, tRNA-ArgtRNA gene374355..374427+
XIIYNCL0008WtD(GUC)L1, tRNA-AsptRNA gene427132..427203+
XIIYNCL0009CtQ(UUG)L, tRNA-GlntRNA gene448650..448721–
XIIYNCL0010CRDN37-1rRNA gene451575..458432–
XIIYNCL0011CETS2-1rRNA gene451575..451785–
XIIYNCL0012CRDN25-1rRNA gene451786..455181–
XIIYNCL0013CITS2-1rRNA gene455182..455413–
XIIYNCL0014CRDN58-1rRNA gene455414..455571–
XIIYNCL0015CITS1-1rRNA gene455572..455932–
XIIYNCL0016CRDN18-1rRNA gene455933..457732–
XIIYNCL0017CETS1-1rRNA gene457733..458432–
XIIYNCL0018WRDN5-1rRNA gene459676..459796+
XIIYNCL0019CETS2-2rRNA gene460712..460922–
XIIYNCL0020CRDN37-2rRNA gene460712..467569–
XIIYNCL0021CRDN25-2rRNA gene460923..464318–
XIIYNCL0022CITS2-2rRNA gene464319..464550–
XIIYNCL0023CRDN58-2rRNA gene464551..464708–
XIIYNCL0024CITS1-2rRNA gene464709..465069–
XIIYNCL0025CRDN18-2rRNA gene465070..466869–
XIIYNCL0026CETS1-2rRNA gene466870..467569–
XIIYNCL0027WRDN5-2rRNA gene468813..468931+
XIIYNCL0028WRDN5-3rRNA gene472465..472583+
XIIYNCL0029WRDN5-4rRNA gene482045..482163+
XIIYNCL0030WRDN5-5rRNA gene485697..485815+
XIIYNCL0031WRDN5-6rRNA gene489349..489469+
XIIYNCL0032CtL(UAG)L1, tRNA-LeutRNA gene592519..592619–
XIIYNCL0033CtI(UAU)L, tRNA-IletRNA gene605300..605432–
XIIYNCL0034WtL(CAA)L, tRNA-LeutRNA gene628383..628497+
XIIYNCL0035CtA(AGC)L, tRNA-AlatRNA gene656934..657006–
XIIYNCL0036CtV(AAC)L, tRNA-ValtRNA gene687859..687932–
XIIYNCL0037WtL(UAG)L2, tRNA-LeutRNA gene732090..732190+
XIIYNCL0038WtI(AAU)L1, tRNA-IletRNA gene734802..734875+
XIIYNCL0039WtX(XXX)LtRNA gene784354..784453+
XIIYNCL0040WtD(GUC)L2, tRNA-AsptRNA gene793918..793989+
XIIYNCL0041CSNR61snR61snoRNA gene794486..794575–
XIIYNCL0042CSNR55snR55snoRNA gene794697..794794–
XIIYNCL0043CSNR57snR57snoRNA gene794937..795024–
XIIYNCL0044WtE(UUC)L, tRNA-GlutRNA gene797178..797249+
XIIYNCL0045WTRR4tR(CCG)L, tRNA-ArgtRNA gene818609..818680+
XIIYNCL0046WSNR44snR44snoRNA gene856710..856920+
XIIYNCL0047WtK(UUU)L, tRNA-LystRNA gene875376..875471+
XIIYNCL0048WSNR34snR34snoRNA gene899180..899382+
XIIYNCL0049CtL(UAA)L, tRNA-LeutRNA gene962972..963055–
XIIYNCL0050CtN(GUU)L, tRNA-AsntRNA gene975983..976056–
XIIYNCL0051CtI(AAU)L2, tRNA-IletRNA gene1052071..1052144–
XIIIYNCM0001WPHO84 lncRNAncRNA gene23564..26578+
XIIIYNCM0002CSNR85snR85snoRNA gene67768..67938–
XIIIYNCM0003WZOD1ncRNA gene91970..92027+
XIIIYNCM0004CtR(UCU)M2, tRNA-ArgtRNA gene131825..131896–
XIIIYNCM0005CSNR54snR54snoRNA gene163535..163620–
XIIIYNCM0006WSUP5tY(GUA)M1, tRNA-TyrtRNA gene168795..168883+
XIIIYNCM0007CtG(GCC)M, tRNA-GlytRNA gene183898..183968–
XIIIYNCM0008CSUF7tP(UGG)M, tRNA-ProtRNA gene196068..196170–
XIIIYNCM0009CtS(AGA)M, tRNA-SertRNA gene259158..259239–
XIIIYNCM0010WtE(UUC)M, tRNA-GlutRNA gene290801..290872+
XIIIYNCM0011WSNR78snR78snoRNA gene297278..297364+
XIIIYNCM0012WSNR77snR77snoRNA gene297506..297593+
XIIIYNCM0013WSNR76snR76snoRNA gene297725..297833+
XIIIYNCM0014WSNR75snR75snoRNA gene297918..298006+
XIIIYNCM0015WSNR74snR74snoRNA gene298138..298225+
XIIIYNCM0016WSNR73snR73snoRNA gene298307..298412+
XIIIYNCM0017WSNR72snR72snoRNA gene298554..298651+
XIIIYNCM0018CtA(AGC)M1, tRNA-AlatRNA gene321147..321219–
XIIIYNCM0019WtF(GAA)M, tRNA-PhetRNA gene352280..352370+
XIIIYNCM0020WtH(GUG)M, tRNA-HistRNA gene363064..363135+
XIIIYNCM0021CtV(AAC)M1, tRNA-ValtRNA gene372445..372518–
XIIIYNCM0022CtW(CCA)M, tRNA-TrptRNA gene379303..379408–
XIIIYNCM0023CtV(AAC)M2, tRNA-ValtRNA gene420588..420661–
XIIIYNCM0024WtD(GUC)M, tRNA-AsptRNA gene463554..463625+
XIIIYNCM0025CtK(CUU)M, tRNA-LystRNA gene480621..480693–
XIIIYNCM0026CSNR24snR24snoRNA gene499984..500072–
XIIIYNCM0027WtL(CAA)M, tRNA-LeutRNA gene504895..505008+
XIIIYNCM0028CEMT4tM(CAU)M, tRNA-MettRNA gene572883..572955–
XIIIYNCM0029CtV(AAC)M3, tRNA-ValtRNA gene586636..586709–
XIIIYNCM0030WSNR83snR83snoRNA gene626349..626654+
XIIIYNCM0031WSNR11snR11snoRNA gene652275..652532+
XIIIYNCM0032CRNA170ncRNA gene667288..667456–
XIIIYNCM0033CtR(UCU)M1, tRNA-ArgtRNA gene747892..747963–
XIIIYNCM0034CSNR86snR86snoRNA gene762110..763113–
XIIIYNCM0035CtA(AGC)M2, tRNA-AlatRNA gene768369..768441–
XIIIYNCM0036CCDC65tQ(CUG)M, tRNA-GlntRNA gene808246..808317–
XIIIYNCM0037WSUP8tY(GUA)M2, tRNA-TyrtRNA gene837928..838016+
XIVYNCN0001WSNR40snR40snoRNA gene89210..89306+
XIVYNCN0002CSUF6tG(UCC)N, tRNA-GlytRNA gene96241..96312–
XIVYNCN0003WtN(GUU)N1, tRNA-AsntRNA gene102716..102789+
XIVYNCN0004WtT(AGU)N1, tRNA-ThrtRNA gene104805..104877+
XIVYNCN0005CSNR19snR19snRNA gene230105..230672–
XIVYNCN0006WtF(GAA)N, tRNA-PhetRNA gene374869..374959+
XIVYNCN0007WtL(CAA)N, tRNA-LeutRNA gene443006..443119+
XIVYNCN0008CtD(GUC)N, tRNA-AsptRNA gene519099..519169–
XIVYNCN0009WtP(UGG)N1, tRNA-ProtRNA gene547094..547196+
XIVYNCN0010CtT(AGU)N2, tRNA-ThrtRNA gene560693..560765–
XIVYNCN0011WtP(UGG)N2, tRNA-ProtRNA gene568115..568217+
XIVYNCN0012CtI(AAU)N1, tRNA-IletRNA gene569867..569940–
XIVYNCN0013WNME1snoRNA gene585587..585926+
XIVYNCN0014WSNR66snR66snoRNA gene586090..586175+
XIVYNCN0015WtI(AAU)N2, tRNA-IletRNA gene602312..602385+
XIVYNCN0016CSUF10tP(AGG)N, tRNA-ProtRNA gene631846..631917–
XIVYNCN0017WtN(GUU)N2, tRNA-AsntRNA gene632599..632672+
XIVYNCN0018WSNR49snR49snoRNA gene716120..716284+
XIVYNCN0019CSNR191snR191snoRNA gene721938..722211–
XIVYNCN0020CtL(UAA)N, tRNA-LeutRNA gene726134..726217–
XVYNCO0001CSUF1tG(UCC)O, tRNA-GlytRNA gene110962..111033–
XVYNCO0002WtT(AGU)O1, tRNA-ThrtRNA gene113802..113874+
XVYNCO0003CSNR58snR58snoRNA gene136088..136183–
XVYNCO0004CtG(GCC)O1, tRNA-GlytRNA gene226611..226681–
XVYNCO0005WtN(GUU)O1, tRNA-AsntRNA gene228331..228404+
XVYNCO0006WSNR81snR81snoRNA gene234346..234546+
XVYNCO0007WSNR50snR50snoRNA gene259489..259578+
XVYNCO0008WtS(GCU)O, tRNA-SertRNA gene274673..274773+
XVYNCO0009WSUF17tG(GCC)O2, tRNA-GlytRNA gene282164..282234+
XVYNCO0010WSUP3tY(GUA)O, tRNA-TyrtRNA gene288192..288280+
XVYNCO0011WtP(UGG)O1, tRNA-ProtRNA gene301097..301198+
XVYNCO0012CtR(ACG)O, tRNA-ARgtRNA gene340299..340371–
XVYNCO0013CtT(AGU)O2, tRNA-ThrtRNA gene354041..354113–
XVYNCO0014CSNR9snR9snoRNA gene407948..408134–
XVYNCO0015CSNR62snR62snoRNA gene409765..409864–
XVYNCO0016WtK(UUU)O, tRNA-LystRNA gene438643..438738+
XVYNCO0017WSUF11tP(UGG)O2, tRNA-ProtRNA gene464450..464551+
XVYNCO0018WtN(GUU)O2, tRNA-AsntRNA gene487439..487512+
XVYNCO0019WtD(GUC)O, tRNA-AsptRNA gene571958..572029+
XVYNCO0020CSUF5tG(CCC)OtRNA gene594354..594425–
XVYNCO0021CtV(AAC)OtRNA gene663812..663885–
XVYNCO0022CSNR36snR36snoRNA gene680685..680866–
XVYNCO0023WIMT1tM(CAU)O1, tRNA-MettRNA gene710201..710272+
XVYNCO0024CSNR35snR35snoRNA gene759326..759529–
XVYNCO0025WSNR17AsnR17asnoRNA gene780107..780596+
XVYNCO0026WSNR8snR8snoRNA gene832332..832521+
XVYNCO0027CSNR31snR31snoRNA gene841958..842182–
XVYNCO0028WSNR5snR5snoRNA gene842403..842606+
XVYNCO0029CtA(UGC)O, tRNA-AlatRNA gene854187..854259–
XVYNCO0030WEMT2tM(CAU)O2, tRNA-MettRNA gene976421..976493+
XVYNCO0031WtP(UGG)O3, tRNA-ProtRNA gene980683..980787+
XVIYNCP0001CtW(CCA)P, tRNA-TrptRNA gene56169..56274–
XVIYNCP0002WGAL4 lncRNAncRNA gene79562..82648+
XVIYNCP0003WSNR59snR59snoRNA gene173827..173904+
XVIYNCP0004CtE(UUC)P, tRNA-GlutRNA gene210192..210263–
XVIYNCP0005CSNR17BsnR17bsnoRNA gene281056..281517–
XVIYNCP0006CIMT2tM(CAU)P, tRNA-MettRNA gene338848..338919–
XVIYNCP0007CtC(GCA)P1, tRNA-CystRNA gene435893..435964–
XVIYNCP0008CtF(GAA)P1, tRNA-PhetRNA gene560198..560289–
XVIYNCP0009WtG(GCC)P1, tRNA-GlytRNA gene572269..572339+
XVIYNCP0010WtK(CUU)P, tRNA-LystRNA gene582062..582134+
XVIYNCP0011CtF(GAA)P2, tRNA-PhetRNA gene622540..622631–
XVIYNCP0012WSUP16tS(UGA)P, tRNA-SertRNA gene689565..689646+
XVIYNCP0013CSNR51snR51snoRNA gene718700..718806–
XVIYNCP0014CSNR70snR70snoRNA gene718887..719050–
XVIYNCP0015CSNR41snR41snoRNA gene719148..719242–
XVIYNCP0016WtT(UGU)P, tRNA-ThrtRNA gene744284..744355+
XVIYNCP0017CtK(UUU)P, tRNA-LystRNA gene769207..769302–
XVIYNCP0018CtC(GCA)P2, tRNA-CystRNA gene775765..775836–
XVIYNCP0019WtN(GUU)P, tRNA-AsntRNA gene810676..810749+
XVIYNCP0020WtI(AAU)P1, tRNA-IletRNA gene819529..819602+
XVIYNCP0021WSNR45snR45snoRNA gene821732..821903+
XVIYNCP0022WtA(AGC)P, tRNA-AlatRNA gene856902..856974+
XVIYNCP0023WtG(GCC)P2, tRNA-GlytRNA gene860379..860449+
XVIYNCP0024CtI(AAU)P2, tRNA-IletRNA gene880296..880369–
MitoYNCQ0001WtP(UGG)Q, tRNA-ProtRNA gene731..802+
MitoYNCQ0002W15S_RRNAQ0020rRNA gene6546..8194+
MitoYNCQ0003WtW(UCA)Q, tRNA-TrptRNA gene9374..9447+
MitoYNCQ0004WtE(UUC)Q, tRNA-GlutRNA gene35373..35444+
MitoYNCQ0005WtS(UGA)Q2, tRNA-SertRNA gene48201..48290+
MitoYNCQ0006W21S_RRNAQ0158rRNA gene58009..62447+
MitoYNCQ0007WtT(UGU)Q1, tRNA-ThrtRNA gene63862..63937+
MitoYNCQ0008WtC(GCA)Q, tRNA-CystRNA gene64415..64490+
MitoYNCQ0009WtH(GUG)Q, tRNA-HistRNA gene64596..64670+
MitoYNCQ0010WtL(UAA)Q, tRNA-LeutRNA gene66095..66179+
MitoYNCQ0011WtQ(UUG)Q, tRNA-GlntRNA gene66210..66285+
MitoYNCQ0012WtK(UUU)Q, tRNA-LystRNA gene67061..67134+
MitoYNCQ0013WtR(UCU)Q1, tRNA-ArgtRNA gene67309..67381+
MitoYNCQ0014WtG(UCC)Q, tRNA-GlytRNA gene67468..67542+
MitoYNCQ0015WtD(GUC)Q, tRNA-AsptRNA gene68322..68396+
MitoYNCQ0016WtS(GCU)Q1, tRNA-SertRNA gene69203..69288+
MitoYNCQ0017WtR(ACG)Q2, tRNA-ArgtRNA gene69289..69362+
MitoYNCQ0018WtA(UGC)Q, tRNA-AlatRNA gene69846..69921+
MitoYNCQ0019WtI(GAU)Q, tRNA-IletRNA gene70162..70237+
MitoYNCQ0020WtY(GUA)Q, tRNA-TyrtRNA gene70824..70907+
MitoYNCQ0021WtN(GUU)Q, tRNA-AsntRNA gene71433..71503+
MitoYNCQ0022WtM(CAU)Q1, tRNA-MettRNA gene72630..72705+
MitoYNCQ0023WtF(GAA)Q, tRNA-PhetRNA gene77431..77505+
MitoYNCQ0024CtT(UAG)Q2, tRNA-ThrtRNA gene78089..78162–
MitoYNCQ0025WtV(UAC)Q, tRNA-ValtRNA gene78533..78608+
MitoYNCQ0026WtM(CAU)Q2, tRNA-MettRNA gene85035..85112+
MitoYNCQ0027WRPM1Q0285ncRNA gene85295..85777+

Various sequence and annotation files are available on SGD’s Downloads site.

Categories: Data updates

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