01333nas a2200145 4500008004100000245008400041210006900125260001200194300001200206490000900218520084900227100002501076700001801101856006801119 2018 eng d00aNanoCAGE-XL: An Approach to High-Confidence Transcription Start Site Sequencing0 aNanoCAGEXL An Approach to HighConfidence Transcription Start Sit c07/2018 a225-2370 v18303 a

Identifying the transcription start sites (TSS) of genes is essential for characterizing promoter regions. Several protocols have been developed to capture the 5′ end of transcripts via Cap-Analysis of Gene Expression (CAGE) or linker-ligation strategies such as Paired-End Analysis of Transcription Start Sites (PEAT), but often require large amounts of tissue. More recently, nanoCAGE was developed for sequencing on the Illumina GAIIx to overcome this limitation. In this chapter, we present the nanoCAGE-XL protocol, the first publicly available adaptation of nanoCAGE for sequencing on recent ultra-high-throughput platforms such as Illumina HiSeq-2000. NanoCAGE-XL provides a method for precise transcription start site identification in large eukaryotic genomes, even in cases where input total RNA quantity is very limited.

1 aIvanchenko, Maria, G1 aMegraw, Molly uhttps://link.springer.com/protocol/10.1007/978-1-4939-8657-6_1302932nas a2200589 4500008004100000245010500041210006900146260001200215300001400227490000800241520124200249100002101491700002301512700001701535700002501552700001601577700002001593700001701613700002301630700002001653700001701673700002201690700002501712700002401737700002501761700002101786700002201807700002101829700002301850700001601873700001901889700001601908700002401924700001801948700002201966700002401988700002602012700001402038700001702052700002402069700002002093700002002113700001902133700001602152700002002168700002202188700001702210700002002227700002102247700002302268856005102291 2017 eng d00aThe Next Generation of Training for Arabidopsis Researchers: Bioinformatics and Quantitative Biology0 aNext Generation of Training for Arabidopsis Researchers Bioinfor c12/2017 a1499-15090 v1753 a
It has been more than 50 years since Arabidopsis (Arabidopsis thaliana) was first introduced as a model organism to understand basic processes in plant biology. A well-organized scientific community has used this small reference plant species to make numerous fundamental plant biology discoveries (Provart et al., 2016). Due to an extremely well-annotated genome and advances in high-throughput sequencing, our understanding of this organism and other plant species has become even more intricate and complex. Computational resources, including CyVerse,3 Araport,4 The Arabidopsis Information Resource (TAIR),5 and BAR,6 have further facilitated novel findings with just the click of a mouse. As we move toward understanding biological systems, Arabidopsis researchers will need to use more quantitative and computational approaches to extract novel biological findings from these data. Here, we discuss guidelines, skill sets, and core competencies that should be considered when developing curricula or training undergraduate or graduate students, postdoctoral researchers, and faculty. A selected case study provides more specificity as to the concrete issues plant biologists face and how best to address such challenges.
1 aFriesner, Joanna1 aAssmann, Sarah, M.1 aBastow, Ruth1 aBailey-Serres, Julia1 aBeynon, Jim1 aBrendel, Volker1 aBuell, Robin1 aBucksch, Alexander1 aBusch, Wolfgang1 aDemura, Taku1 aDinneny, Jose, R.1 aDoherty, Colleen, J.1 aEveland, Andrea, L.1 aFalter-Braun, Pascal1 aGehan, Malia, A.1 aGonzales, Michael1 aGrotewold, Erich1 aGutierrez, Rodrigo1 aKramer, Ute1 aKrouk, Gabriel1 aMa, Shisong1 aMarkelz, R.J., Cody1 aMegraw, Molly1 aMeyers, Blake, C.1 aMurray, James, A.H.1 aProvart, Nicholas, J.1 aRhee, Sue1 aSmith, Roger1 aSpalding, Edgar, P.1 aTaylor, Crispin1 aTeal, Tracy, K.1 aTor, Keiko, U.1 aTown, Chris1 aVaughn, Matthew1 aVierstra, Richard1 aWare, Doreen1 aWilkins, Olivia1 aWilliams, Cranos1 aBrady, Siobhan, M. uhttp://www.plantphysiol.org/content/175/4/149902178nas a2200277 4500008004100000022001400041245011900055210006900174260000900243300000800252490000700260520133000267653001601597653001701613653001801630653001901648653001601667653003001683653002701713653001301740653003401753100002101787700002501808700001801833856004901851 2015 eng d a1471-216400aNanoCAGE-XL and CapFilter: an approach to genome wide identification of high confidence transcription start sites.0 aNanoCAGEXL and CapFilter an approach to genome wide identificati c2015 a5970 v163 a

BACKGROUND: Identifying the transcription start sites (TSS) of genes is essential for characterizing promoter regions. Several protocols have been developed to capture the 5' end of transcripts via Cap Analysis of Gene Expression (CAGE) or linker-ligation strategies such as Paired-End Analysis of Transcription Start Sites (PEAT), but often require large amounts of tissue. More recently, nanoCAGE was developed for sequencing on the Illumina GAIIx to overcome these difficulties.

RESULTS: Here we present the first publicly available adaptation of nanoCAGE for sequencing on recent ultra-high throughput platforms such as Illumina HiSeq-2000, and CapFilter, a computational pipeline that greatly increases confidence in TSS identification. We report excellent gene coverage, reproducibility, and precision in transcription start site discovery for samples from Arabidopsis thaliana roots.

CONCLUSION: nanoCAGE-XL together with CapFilter allows for genome wide identification of high confidence transcription start sites in large eukaryotic genomes.

[Link to Protocol, Additional Data, and Supplementary Materials]

[Link to CapFilter Software]

10aArabidopsis10aGenes, Plant10aGenome, Plant10aNanotechnology10aPlant Roots10aPromoter Regions, Genetic10aSequence Analysis, DNA10aSoftware10aTranscription Initiation Site1 aCumbie, Jason, S1 aIvanchenko, Maria, G1 aMegraw, Molly uhttps://megraw.cgrb.oregonstate.edu/node/311