Additional file 1: Table S1. of 6S RNA plays a role in recovery from nitrogen depletion in Synechocystis sp. PCC 6803

Sequences of primers used for construction and PCR-based verification of ΔssaA- and HIS-mutant strains, as well as for PCR-based generation of riboprobe templates. Figure S1. Pairwise comparison of whole cell absorbance spectra of Synechocystis 6803 WT and ΔssaA strain shown in Fig. 2c. Figure S2.Photosynthetic parameters of wild type (WT) and ∆ssaA mutant during nitrogen starvation. Figure S3. Verification of the genetic complementation of ssaA gene disruption. Figure S4. Pigment analysis of WT and ΔssaA. Figure S5. Glycogen consumption during recovery from nitrogen starvation. Figure S6. Volcanoplot showing the gene expression differences in WT and ΔssaA after prolonged nitrogen depletion and before nitrogen re-addition (t1 = 0 h + N). Figure S7. Expression patterns of transcripts with significantly altered levels in the ΔssaA mutant strain at early recovery from nitrogen depletion (t2 = 1 h + N). Figure S8. Expression patterns of transcripts with significantly altered levels in the ΔssaA mutant strain at recovery from nitrogen depletion (t3 = 4 h + N.). Figure S9. Expression patterns of transcripts with significantly altered levels in the ΔssaA mutant strain at recovery from nitrogen depletion (t4 = 22 h + N). Figure S10. Microarray data analysis showing exemplary details of gene expression in Synechocystis 6803 WT and ∆ssaA strain (depicted as d6S) at time points t1 = 0 h + N (7d –N); t2 = 1 h + N, t3 = 4 h + N and t4 = 22 h + N. Figure S11. Validation of Microarray data analysis. Expression of SyR11 in Synechocystis 6803. (DOCX 1909 kb

Supplementary Datasets for "The Primary Transcriptome of the Fast-Growing Cyanobacterium Synechococcus elongatus UTEX 2973"

<p>This file set contains supplementary dataset for paper "Tan, X.<i> et al.</i> <b>The Primary Transcriptome of the Fast-Growing Cyanobacterium Synechococcus elongatus UTEX 2973</b>. (doi.org/10.1186/s13068-018-1215-8)." Please refer to this paper for more information.</p><p><b><u><br></u></b></p><p><b><u>File Description:</u></b></p><p><b><u><br></u></b></p><p><b>Supplementary Dataset 1:</b> Genome-wide visualization of predicted TSS and coverage for the genome of <i>Synechococcus elongatus </i>UTEX 2973.</p><p><b>Supplementary Dataset 2: </b>Genome annotation of <i>Synechococcus elongatus </i>UTEX 2973 with predicted TSS positions.</p><div><br></div><div><br></div><div><div><b><u>How To Cite This Dataset:</u></b></div><div><br></div><div>If you find this dataset useful to your research, please consider to cite the main paper:</div><div><br></div><div>Tan, X., Hou, S., Song, K., Georg, J., Klähn, S., Lu, X., & Hess, W. R. (2018). <b>The primary transcriptome of the fast-growing cyanobacterium <i>Synechococcus elongatus</i> UTEX 2973</b>. <i>Biotechnology for Biofuels</i>, <i>11</i>(1), 218.<br></div><div><br></div></div

MOESM1 of The primary transcriptome of the fast-growing cyanobacterium Synechococcus elongatus UTEX 2973

Additional file 1: Table S1. All information of each identified TSS. Table S2. Leaderless transcripts and mis-annotated genes. Table S3. GO terms enrichments. Table S4. KEGG pathway enrichments. Table S5. Top 10 up-regulated and top 10 down-regulated transcripts. Table S6. Transcript changes of genes associated with photosynthesis and phycobilisomes. Table S7. Transcript changes of genes associated with central metabolisms. Table S8. asRNAs and their potential target genes. Table S9. List of primers used for the amplification of probe templates

Additional file 3 of 6S RNA plays a role in recovery from nitrogen depletion in Synechocystis sp. PCC 6803

Raw data of microarray analysis of Synechocystis 6803 wild type (WT) and ΔssaA (d6S) strain. (XLSX 5837 kb

Image_3_GLASSgo – Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence.PDF

<p>Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.</p

Data_Sheet_1_GLASSgo – Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence.ZIP

<p>Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.</p

Image_1_GLASSgo – Automated and Reliable Detection of sRNA Homologs From a Single Input Sequence.TIF

<p>Bacterial small RNAs (sRNAs) are important post-transcriptional regulators of gene expression. The functional and evolutionary characterization of sRNAs requires the identification of homologs, which is frequently challenging due to their heterogeneity, short length and partly, little sequence conservation. We developed the GLobal Automatic Small RNA Search go (GLASSgo) algorithm to identify sRNA homologs in complex genomic databases starting from a single sequence. GLASSgo combines an iterative BLAST strategy with pairwise identity filtering and a graph-based clustering method that utilizes RNA secondary structure information. We tested the specificity, sensitivity and runtime of GLASSgo, BLAST and the combination RNAlien/cmsearch in a typical use case scenario on 40 bacterial sRNA families. The sensitivity of the tested methods was similar, while the specificity of GLASSgo and RNAlien/cmsearch was significantly higher than that of BLAST. GLASSgo was on average ∼87 times faster than RNAlien/cmsearch, and only ∼7.5 times slower than BLAST, which shows that GLASSgo optimizes the trade-off between speed and accuracy in the task of finding sRNA homologs. GLASSgo is fully automated, whereas BLAST often recovers only parts of homologs and RNAlien/cmsearch requires extensive additional bioinformatic work to get a comprehensive set of homologs. GLASSgo is available as an easy-to-use web server to find homologous sRNAs in large databases.</p

Essays on Philanthropy

The basic concern of any empirical work is to employ statistical data that correspond to the notion of the theoretical variables in the model. The problems and economic consequences connected with the measurement of selected economic variables are the focus of this thesis. It consists of three chapters that in succession analyze the issues associated with the measurement of economic growth, multi-factor productivity and capital input into production. The first chapter looks into the differences among the growth rates of GDP per capita based on data from the three most commonly used databases, namely International Financial Statistics, World Development Indicators and Penn World Table. Using a wide international dataset, we find significant differences in the growth rates that are mainly due to the adjustment for cross-country comparability of GDP per capita levels. Importantly, these differences are correlated with the level of development. We replicate six recent studies of growth determinants and find their results sensitive to the choice of data. The second chapter analyses the sensitivity of calculated multi-factor productivity (MFP) growth to assumptions of growth accounting, concentrating on the measurement of quantity, composition and the respective shares of labor and capital inputs, and..