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By using clinically discarded human embryos (Methods), we generated scCAT-seq profiles for a total of 110 individual cells, and successfully obtained 29 quality-filtered profiles from the morula stage and 43 from the blastocyst stage (success rate 65.5%) (Fig. 3a, Supplementary Figure 4a and Supplementary Data 1).

For evaluation of the feature selection methods, we generated artificial regression data according to the following procedure.

Materials and methods We generated constitutive Trem-1−/− as well as endothelium-conditional Trem-1 KO mice and submitted them to polymicrobial sepsis through CLP.

To achieve a better view of how coverage depth was distributed for each of the enrichment methods, we generated kernel density plots for coverage depths across all targeted basepairs across all samples (Figure 3).

By combining genomic data from both the GRO (Genomic Run-on) and the RNA pol ChIP-on-chip methods we generated a new, more accurate nascent transcription rate dataset.

Using PAUP 4.0b10 software and maximum parsimony methods, we generated phylogenetic relationships (8 ).

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First, as indicated in Expressions [1] [3] in Methods, we generate relative, not absolute, measures of the magnitude of R0.

In order to evaluate the performance of the different network reconstruction methods we generate an ensemble of 100 synthetic networks.

By integrating results from different methods, we generate the consensus-based final predictions for local sequence features, three-dimensional structure and function.

Using our modified library generation method, we generated genome-wide DNA methylomes for human sperm, oocytes, 8-cell embryos, morula, ICM, and 6-week embryos as well as the full-term placenta at single-base resolution (Supplementary Table S1).

Herein, using our modified MethylC-Seq library generation method and published post-bisulphite adapter-tagging (PBAT) method, we generated genome-wide DNA methylomes of human gametes and early embryos at single-base resolution and compared them with mouse methylomes.

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