Carbon isotopes can be used to interpret sea‐level changes during deposition, but the accuracy of sea‐level changes indicated by shallow‐water carbonates remains unclear. We carried out sedimentary microfacies and stable isotope analysis of carbonate rocks in Upper Ordovician Lianglitage Formation in Tazhong area, Tarim Basin, to examine the response of isotopes to high‐frequency cycles of shallow‐water carbonate rocks. The Lianglitage limestones can be divided into four types of microfacies that were deposited in reef and shoal environments on a platform margin. A total of sixty‐one (61) selected limestone samples have the δ13C value varying from 0.5993‰ to 1.6228‰ (average 1.1364‰). The extremely low correlation coefficient of carbon and oxygen isotopes indicate that the samples can represent the deposition seawater, thus the estimated Z value and temperature show that the Lianglitage Formation was deposited in the normal marine environment in a tropical to subtropical zone. The Lianglitage Formation shows a clear deposition trend from tidal flat to reef shoal and then to an open platform. The sedimentary environment controls the difference in carbon and oxygen isotopes of shallow‐water carbonates through the rate of burial of organic carbon, and thus the δ13C tend to increase when sea level rise. Hence the carbon isotopic composition of shallow‐water carbonate rocks can reflect sea‐level changes. The δ13C of Lianglitage Formation in well TZ72 shows four sedimentary cycles (20–40‐meters‐thick) controlled by sea‐level changes, which were also recorded in this Formation from other parts of the Tarim Basin.

Background: Mining key transcription factors (TFs) in genome-wide transcriptome profiling data has been an active research area for many years and it has been partially solved by mathematically modelling the ranking orders of genes in the target gene-set for the TF of interest in the gene-list ranked by expression values, called gene-set enrichment analysis (GSEA). However, in some application scenarios the gene-set itself also has a rank attribute, such as the putative target gene-set predicted by the Grit software and other alternatives like FIMO and Pscan. New algorithms must be developed to analyze these data properly. Methodology/Principal Findings: By implementing the weighted Kendall's tau statistic, we proposed a method for genome-wide transcriptome profiling data mining that can identify the key TFs orchestrating a profile. Theoretical properties of the proposed method were established, and its advantages over the GSEA approach were demonstrated when analyzing the RNA-Atlas data-sets. The results showed that the top-rated TFs by our method always have experimentally supported evidences in the literatures. Benchmarking using gene ontology (GO) annotations in the AmiGO database indicated that the geometry performance (SQR_P) of our method is higher than GSEA in more than 14% of the cases. Significance: The developed method is suitable for analyzing the significance of overrepresentation of ranked gene-sets in a ranked gene-list. A software implementing the method, called "Flaver", was developed and is publicly available at http://www.thua45.cn/flaver under an academic free license.