Sure, we would recommend using afni_proc.py to set up your analysis. I would start by looking at examples 11 and 11b here in the afni_proc.py help:
[afni.nimh.nih.gov]

There is the APMULTI demo, for afni_proc.py processing of multi-echo FMRI (resting state) data, which also contains a script for single-echo (= "standard acquisition") FMRI processing that would be useful to you; run the following to download+unpack it:
@Install_APMULTI_Demo
The README* files there provide more information. There are scripts_desktop/ and scripts_biowulf/ versions of the scripts, for whether you aim to process on a desktop computer or a cluster, respectively. The do_*tcsh scripts have the single subject processing commands, and the corresponding run_*tcsh scripts contain the pieces to loop over everything. You could organize your starting data similarly to how the data_00* tree looks (with more subjects, in your case), and then apply the scripts pretty directly. You would likely want to use the do_*ssw*.tcsh script to skullstrip your anatomical and align it to standard space; perhaps the do_*fs*.tcsh script would be of interest for running FreeSurfer's recon-all for parcellating the data and if you want tissue-based regressors; and the do_20_ap_se.tcsh contains the single-echo afni_proc.py processing suggestion, as a starting point for creating your own analysis.

You have to decide a bit about what kind of processing choices you want: are you going to do ROI-based processing (in which case, pick an appropriate template and atlas for that, and don't blur during processing), or voxelwise (in which case, likely still pick an appropriate template, but then decide how much blurring you want). Do you want to use tissue-based regressors (like fast anaticor)? Do you want to bandpass (lots of resting state studies do, but it might be better not to, to preserve degrees of freedom and higher-frequency useful info)?

Some papers about resting state processing:
+ Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571-582. doi:10.1016/j.neuroimage.2010.04.246
[pubmed.ncbi.nlm.nih.gov]
+ Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI
[www.hindawi.com]

Some papers about why you likely wouldn't want to include global signal regression:
[afni.nimh.nih.gov]

Please feel free to ping back with more questions/comments here, too.

--pt