diff --git a/dcicutils/ff_utils.py b/dcicutils/ff_utils.py
index 02264d495..b73e457cb 100644
--- a/dcicutils/ff_utils.py
+++ b/dcicutils/ff_utils.py
@@ -457,65 +457,179 @@ def faceted_search(key=None, ff_env=None, item_type=None, **kwargs):
     return search_metadata(search, ff_env=ff_env, key=key)
 
 
-def get_associated_qc_metrics(uuid, key=None, ff_env=None,
-                              exclude_raw_files=True,
-                              exclude_supplementary_files=True):
+def fetch_files_qc_metrics(data, associated_files=['processed_files'],
+                           ignore_typical_fields=True,
+                           key=None, ff_env=None):
     """
-    Given a uuid of an experiment set, return a dictionary of uuid : item
-    mappings of quality metric items.
+    Utility function to grap all the qc metrics from associated types of file such as:
+    'proccessed_files', 'other_processed_files', 'files'
+    Args:
+        data: the metadata of a ExperimentSet or Experiment
+        associated_files: a list of the types of the files fields the qc metrics will be extracted from:
+            examples are = ['files', 'processed_files', 'other_processed_files']
+        ignore_typical_fields: flag to ignore 4DN custom fields from the qc metric object
+
+    Returns:
+        a dictionary of dictionaries containing the qc_metric information
+    """
+    qc_metrics = {}
+
+    if ignore_typical_fields:
+        ignorable_qc_fields = ['contributing_labs', 'schema_version', 'external_references', '@context', 'aliases',
+                               'project_release', 'award', 'principals_allowed', 'validation-errors',
+                               'last_modified', 'slope', '@id', 'aggregated-items', 'status', 'public_release',
+                               'actions', 'submitted_by', 'convergence', 'lab', 'date_created', 'uuid']
+    else:
+        ignore_typical_fields = []
+    # for each file
+    for associated_file in associated_files:
+        if associated_file in data:
+            if associated_file == 'other_processed_files':
+                target_files = []
+                for entry in data[associated_file]:
+                    if 'files' in entry:
+                        target_files = target_files + entry['files']
+
+            else:
+                target_files = data[associated_file]
+
+            for entry in target_files:
+                if entry.get('quality_metric'):
+                    # check if it is a list of qc metrics
+                    if entry['quality_metric']['display_title'].startswith('QualityMetricQclist'):
+                        qc_metric_list = get_metadata(entry['quality_metric']['uuid'], key=key, ff_env=ff_env)
+                        if not qc_metric_list.get('qc_list'):
+                            continue
+                        for qc in qc_metric_list['qc_list']:
+                            qc_uuid = qc['value']['uuid']
+                            qc_meta = get_metadata(qc_uuid, key=key, ff_env=ff_env)
+                            qc_values = {k: v for k, v in qc_meta.items() if k not in ignorable_qc_fields}
+                            source_file_association = associated_file if associated_file != 'files' else 'raw_file'
+                            source_file = entry['accession']
+                            source_file_type = entry['file_type_detailed']
+                            qc_info = {
+                                qc_uuid: {'values': qc_values,
+                                          'source_file_association': source_file_association,
+                                          'source_file': source_file,
+                                          'source_file_type': source_file_type
+                                          }
+                                        }
+                            qc_metrics.update(qc_info)
+
+                    else:
+                        qc_uuid = entry['quality_metric']['uuid']
+                        qc_meta = get_metadata(qc_uuid, key=key, ff_env=ff_env)
+                        qc_values = {k: v for k, v in qc_meta.items() if k not in ignorable_qc_fields}
+                        source_file_association = associated_file if associated_file != 'files' else 'raw_file'
+                        source_file = entry['accession']
+                        source_file_type = entry['file_type_detailed']
+                        qc_info = {
+                            qc_uuid: {'values': qc_values,
+                                      'source_file_association': source_file_association,
+                                      'source_file': source_file,
+                                      'source_file_type': source_file_type
+                                      }
+                                    }
+                        qc_metrics.update(qc_info)
+    return qc_metrics
+
+
+def get_associated_qc_metrics(uuid, key=None, ff_env=None, include_processed_files=True,
+                              include_raw_files=False,
+                              include_supplementary_files=False):
+    """
+    Given a uuid of an experimentSet return a dictionary of dictionaries with each dictionary
+    representing a quality metric.
 
     Args:
-        exclude_raw_files: if False will provide QC metrics on raw files as well
-                           Default: True
-        exclude_supplementary_files: if False will also give QC's associated with
-                                     non-processed files. Default: True
+        include_processed_files: if False will exclude QC metrics on processed files
+                                Default: True
+        include_raw_files: if True will provide QC metrics on raw files as well
+                           Default: False
+        include_supplementary_files: if True will also give QC's associated with
+                                     non-processed files. Default: False
+    Returns:
+        a dictionary of dictionaries with the following structure:
+            {<qc_metric_uuid>}:{
+                'values': the values of the qc_metric object>,
+                'source_file_association': <the file class (processed_file or raw_files)>,
+                'source_file': <the accession of the file that the qc is linked to>,
+                'source_file_type': <the description of the file that the qc is linked to>,
+                'experiment_description': <the description of the experiment or experimentset>
+                'organism': <the organism>
+                'experiment_type': <the experiment type (in situ Hi-C, ChIP-seq)>,
+                'experiment_subclass': <the experiment subclass (Hi-C)>,
+                'source_experiment': <the experiment the qc is linked to (if apply)>,
+                'source_experimentSet': <the experimentSet the qc is linked to>,
+                'biosource_summary': <the experiment biosource>
+                }
+            }
     """
     result = {}
+    associated_files = []
+
+    # Additional information to include in the results for interpretation
+    organism = None
+    experiment_type = None
+    experiment_subclass = None
+    description = None
+    biosource_summary = None
+
     resp = get_metadata(uuid, key=key, ff_env=ff_env)
 
-    # handle all 'processed_files' by default
-    if 'processed_files' in resp:
-        for entry in resp['processed_files']:
-            if 'quality_metric' not in entry:
-                continue
-            uuid = entry['quality_metric']['uuid']
-            result[uuid] = get_metadata(uuid, key=key, ff_env=ff_env)
-
-    # handle 'other_processed_files' if we are doing supplementary files
-    if 'other_processed_files' in resp and not exclude_supplementary_files:
-        for entry in resp['processed_files']:
-            if 'quality_metric' not in entry:
-                continue
-            uuid = entry['quality_metric']['uuid']
-            result[uuid] = get_metadata(uuid, key=key, ff_env=ff_env)
-
-    # check 'experiment_in_set' as these can contain things too
-    if 'experiments_in_set' in resp:
+    # Checks wheter the input is a experiment or experimentset otherwise throws an error
+    if 'ExperimentSet' not in resp['@type']:
+        raise TypeError('Expected ExperimentSet')
+
+    # verifies what category of files to include (processed_files, other_processed_files, files)
+    if include_processed_files:
+        associated_files.append('processed_files')
+    if include_supplementary_files:
+        associated_files.append('other_processed_files')
+    if include_raw_files:
+        associated_files.append('files')
+
+    if not associated_files:
+        return result
+
+    # If it is an experimentset, get qc_metrics for the experiments in the experiment set
+    if resp.get('experiments_in_set'):
+        organism = resp['experiments_in_set'][0]['biosample']['biosource'][0]['individual']['organism']['name']
+        experiment_type = resp['experiments_in_set'][0]['experiment_type']['display_title']
+        experiment_subclass = resp['experiments_in_set'][0]['experiment_type']['assay_subclass_short']
+        biosource_summary = resp['experiments_in_set'][0]['biosample']['biosource_summary']
+
         for exp in resp['experiments_in_set']:
+            exp_description = exp['display_title']
+            exp_qc_metrics = fetch_files_qc_metrics(exp, associated_files, key=key, ff_env=ff_env)
+            meta_info = {'experiment_description': exp_description,
+                         'organism': organism,
+                         'experiment_type': experiment_type,
+                         'experiment_subclass': experiment_subclass,
+                         'source_experiment': exp['accession'],
+                         'source_experimentSet': resp['accession'],
+                         'biosource_summary': biosource_summary
+                         }
+            if exp_qc_metrics:
+                for exp_qc_metric in exp_qc_metrics.values():
+                    exp_qc_metric.update(meta_info)
+                result.update(exp_qc_metrics)
+
+    description = resp.get('dataset_label', None)
+    ES_qc_metrics = fetch_files_qc_metrics(resp, associated_files, key=key, ff_env=ff_env)
+    if ES_qc_metrics:
+        meta_info = {'experiment_description': description,
+                     'organism': organism,
+                     'experiment_type': experiment_type,
+                     'experiment_subclass': experiment_subclass,
+                     'source_experiment': None,
+                     'source_experimentSet': resp['accession'],
+                     'biosource_summary': biosource_summary
+                     }
+        for qc_metric in ES_qc_metrics.values():
+            qc_metric.update(meta_info)
+        result.update(ES_qc_metrics)
 
-            # handle all 'processed_files' by default
-            if 'processed_files' in exp:
-                for entry in exp['processed_files']:
-                    if 'quality_metric' not in entry:
-                        continue
-                    uuid = entry['quality_metric']['uuid']
-                    result[uuid] = get_metadata(uuid, key=key, ff_env=ff_env)
-
-            # handle 'other_processed_files' if we're doing supplementary files
-            if 'other_processed_files' in exp and not exclude_supplementary_files:
-                for entry in exp['processed_files']:
-                    if 'quality_metric' not in entry:
-                        continue
-                    uuid = entry['quality_metric']['uuid']
-                    result[uuid] = get_metadata(uuid, key=key, ff_env=ff_env)
-
-            # handle 'files' only if we are doing raw files
-            if 'files' in exp and not exclude_raw_files:
-                for entry in exp['files']:
-                    if 'quality_metric' not in entry:
-                        continue
-                    uuid = entry['quality_metric']['uuid']
-                    result[uuid] = get_metadata(uuid, key=key, ff_env=ff_env)
     return result
 
 
diff --git a/test/data_files/qc_metrics.json b/test/data_files/qc_metrics.json
new file mode 100644
index 000000000..7b0b816ad
--- /dev/null
+++ b/test/data_files/qc_metrics.json
@@ -0,0 +1,5 @@
+{
+	"762d3cc0-fcd4-4c1a-a99f-124b0f371690": {"source_file_type": "contact list-replicate", "values":{"Total Reads": 9087}},
+	"9b0b1733-0f17-420e-9e38-1f58ba993c54": {"source_file_type": "contact list-replicate (pairs)"}, "values":{"Cis/Trans":0.3}
+
+}
diff --git a/test/data_files/test_experiment_set.json b/test/data_files/test_experiment_set.json
new file mode 100644
index 000000000..9feda5229
--- /dev/null
+++ b/test/data_files/test_experiment_set.json
@@ -0,0 +1,3792 @@
+{
+	"@context": "/terms/",
+	"uuid": "6ba6a5df-dac5-4111-b5f0-aaaaaaaaaaaa",
+	"external_references": [],
+	"completed_processes": ["HiC_Pipeline_0.2.6"],
+	"submitted_by": {
+		"error": "no view permissions"
+	},
+	"publications_of_set": [{
+		"@id": "/publications/b13590b2-a341-4e5e-ad5e-72e233b32e9d/",
+		"uuid": "b13590b2-a341-4e5e-ad5e-72e233b32e9d",
+		"principals_allowed": {
+			"view": ["system.Everyone"],
+			"edit": ["group.admin"]
+		},
+		"@type": ["Publication", "Item"],
+		"abstract": "Over the past decade, 3C-related methods, complemented by increasingly detailed microscopic views of the nucleus, have provided unprecedented insights into chromosome folding in vivo. Here, to overcome the resolution limits inherent to the majority of genome-wide chromosome architecture mapping studies, we extend a recently-developed Hi-C variant, Micro-C, to map chromosome architecture at nucleosome resolution in human embryonic stem cells and fibroblasts. Micro-C maps robustly capture well-described features of mammalian chromosome folding including A/B compartment organization, topologically associating domains (TADs), and cis interaction peaks anchored at CTCF binding sites, while also providing a detailed 1-dimensional map of nucleosome positioning and phasing genome-wide. Compared to high-resolution in situ Hi-C, Micro-C exhibits substantially improved signal-to-noise with an order of magnitude greater dynamic range, enabling not only localization of domain boundaries with single-nucleosome accuracy, but also resolving more than 20,000 additional looping interaction peaks in each cell type. Intriguingly, many of these newly-identified peaks are localized along stripe patterns and form transitive grids, consistent with their anchors being pause sites impeding the process of cohesin-dependent loop extrusion. Together, our analyses provide the highest resolution maps of chromosome folding in human cells to date, and provide a valuable resource for studies of chromosome folding mechanisms.",
+		"journal": "bioRxiv",
+		"title": "Ultrastructural details of mammalian chromosome architecture",
+		"date_published": "2019-05-17",
+		"display_title": "Nils Krietenstein et al. (2019) Ultrastructural details of mammalian chromosome architecture",
+		"authors": ["Nils Krietenstein", "Sameer Abraham", "Sergey V. Venev", "Nezar Abdennur", "Johan Gibcus", "Tsung-Han S. Hsieh", "Krishna Mohan Parsi", "Liyan Yang", "Ren Maehr", "Leonid A. Mirny", "Job TEST", "Oliver J. Rando"]
+	}],
+	"alternate_accessions": [],
+	"aliases": ["TEST-lab:ExperimentSet_U54_U54-ESC4DN-FA-DpnII-2017524"],
+	"condition": "Enzyme Dpn II - cells cultured following 4DN SOP",
+	"@id": "/experiment-set-replicates/TESTS2AAAAAA/",
+	"tags": ["4DN Joint Analysis 2018", "2018_01_public", "2018_01_internal", "2018_01_internal_JA"],
+	"principals_allowed": {
+		"view": ["system.Everyone"],
+		"edit": ["group.admin"]
+	},
+	"static_content": [{
+		"location": "tab:processed-files",
+		"description": "auto_generated_higlass_view_config",
+		"content": {
+			"status": "released",
+			"name": "13e2ca00-957a-47ce-bdf4-080af3a67b9c",
+			"uuid": "13e2ca00-957a-47ce-bdf4-080af3a67b9c",
+			"@type": ["HiglassViewConfig", "UserContent", "Item"],
+			"title": "TESTS2AAAAAA - Processed files",
+			"display_title": "TESTS2AAAAAA - Processed files",
+			"contributing_labs": [],
+			"lab": {
+				"@id": "/labs/job-TEST-lab/",
+				"uuid": "3c577664-affb-41c4-bf27-9e21c2fc1554",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin", "submits_for.3c577664-affb-41c4-bf27-9e21c2fc1554"]
+				},
+				"@type": ["Lab", "Item"],
+				"display_title": "Job TEST, UMMS"
+			},
+			"@id": "/higlass-view-configs/13e2ca00-957a-47ce-bdf4-080af3a67b9c/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin", "userid.986b362f-4eb6-4a9c-8173-3ab267307e3a"]
+			},
+			"filetype": "HiglassViewConfig",
+			"award": {
+				"@id": "/awards/1U54DK107980-01/",
+				"uuid": "ae6c618f-7a8c-441e-a886-e30bbbe591da",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"@type": ["Award", "Item"],
+				"display_title": "CENTER FOR 3D STRUCTURE AND PHYSICS OF THE GENOME"
+			},
+			"description": "TESTS2AAAAAA (in situ Hi-C on H1-hESC (Tier 1) with DpnII): 4DNFI6HDY7WZ"
+		}
+	}],
+	"aggregated-items": {
+		"tissue": [{
+			"embedded_path": "experiments_in_set.biosample.biosource.tissue",
+			"parent": "/biosources/4DNSRV3SKQ8M/",
+			"item": {
+				"preferred_name": "stem cell"
+			}
+		}],
+		"badges": [{
+			"embedded_path": "badges",
+			"parent": "/experiment-set-replicates/TESTS2AAAAAA/",
+			"item": {
+				"messages": ["Replicate set contains only a single biological replicate"],
+				"badge": {
+					"commendation": null,
+					"warning": "Replicate Numbers",
+					"@id": "/badges/replicate-numbers/",
+					"uuid": "24a64a84-3c33-4d76-aaf2-e5ef45eff347",
+					"badge_icon": "/static/img/badges/replicates-orange-circle.svg",
+					"description": "Issues with replicate numbers"
+				}
+			}
+		}, {
+			"embedded_path": "experiments_in_set.biosample.badges",
+			"parent": "/biosamples/4DNBS3O2FNJO/",
+			"item": {
+				"messages": ["Biosample missing doubling_number", "Biosample missing morphology_image"],
+				"badge": {
+					"commendation": null,
+					"warning": "Biosample Metadata Incomplete",
+					"@id": "/badges/biosample-metadata-incomplete/",
+					"uuid": "2b2cc7ff-b7a8-4138-9a6c-22884fc71690",
+					"badge_icon": "/static/img/badges/biosample-icon.svg",
+					"description": "Biosample is missing metadata information required as part of the standards implemented by the 4DN Samples working group."
+				}
+			}
+		}]
+	},
+	"dataset_label": "Hi-C on H1 cells - protocol variations",
+	"validation-errors": [],
+	"replicate_exps": [{
+		"tec_rep_no": 1,
+		"replicate_exp": {
+			"@id": "/experiments-hi-c/4DNEXENKINA2/",
+			"uuid": "eca3ac9b-9dc4-4891-861d-771279b7f9b1",
+			"accession": "4DNEXENKINA2",
+			"@type": ["ExperimentHiC", "Experiment", "Item"],
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"display_title": "in situ Hi-C on H1-hESC (Tier 1) with DpnII - 4DNEXENKINA2"
+		},
+		"bio_rep_no": 1
+	}, {
+		"tec_rep_no": 2,
+		"replicate_exp": {
+			"@id": "/experiments-hi-c/4DNEXZJZ5EBZ/",
+			"uuid": "c00ecfa0-7e3d-43af-b1c2-091aa9379594",
+			"accession": "4DNEXZJZ5EBZ",
+			"@type": ["ExperimentHiC", "Experiment", "Item"],
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"display_title": "in situ Hi-C on H1-hESC (Tier 1) with DpnII - 4DNEXZJZ5EBZ"
+		},
+		"bio_rep_no": 1
+	}],
+	"experimentset_type": "replicate",
+	"status": "released",
+	"number_of_experiments": 2,
+	"project_release": "2017-07-26",
+	"@type": ["ExperimentSetReplicate", "ExperimentSet", "Item"],
+	"badges": [{
+		"messages": ["Replicate set contains only a single biological replicate"],
+		"badge": {
+			"warning": "Replicate Numbers",
+			"@id": "/badges/replicate-numbers/",
+			"uuid": "24a64a84-3c33-4d76-aaf2-e5ef45eff347",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"display_title": "Replicate Numbers",
+			"@type": ["Badge", "Item"],
+			"badge_icon": "/static/img/badges/replicates-orange-circle.svg",
+			"badge_classification": "Warning",
+			"description": "Issues with replicate numbers",
+			"title": "Replicate Numbers"
+		}
+	}],
+	"other_processed_files": [{
+		"type": "archived",
+		"files": [{
+			"status": "released",
+			"uuid": "aff4c78b-b07a-4fb0-b568-388f0a8ff0e3",
+			"href": "/files-processed/4DNFI3RIKRUJ/@@download/4DNFI3RIKRUJ.hic",
+			"@type": ["FileProcessed", "File", "Item"],
+			"display_title": "4DNFI3RIKRUJ.hic",
+			"@id": "/files-processed/4DNFI3RIKRUJ/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"last_modified": {
+				"date_modified": "2018-09-18T04:20:33.471425+00:00"
+			},
+			"file_format": {
+				"@id": "/file-formats/hic/",
+				"uuid": "d13d11cf-218e-4f61-bbf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"@type": ["FileFormat", "Item"],
+				"display_title": "hic"
+			},
+			"accession": "4DNFI3RIKRUJ",
+			"file_size": 10960590432,
+			"file_type_detailed": "contact matrix (hic)"
+		}, {
+			"status": "released",
+			"uuid": "d0237baf-973a-45f4-acb3-33ee6cf7c7f6",
+			"href": "/files-processed/4DNFI65CBIKX/@@download/4DNFI65CBIKX.mcool",
+			"@type": ["FileProcessed", "File", "Item"],
+			"genome_assembly": "GRCh38",
+			"display_title": "4DNFI65CBIKX.mcool",
+			"@id": "/files-processed/4DNFI65CBIKX/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"last_modified": {
+				"date_modified": "2019-05-08T18:39:20.016666+00:00"
+			},
+			"file_format": {
+				"@id": "/file-formats/mcool/",
+				"uuid": "d13d06cf-218e-4f61-ccf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"@type": ["FileFormat", "Item"],
+				"display_title": "mcool"
+			},
+			"accession": "4DNFI65CBIKX",
+			"file_size": 13385314415,
+			"higlass_uid": "fwgPd2PMTW6E5Clf7zIK6Q",
+			"file_type_detailed": "contact matrix (mcool)"
+		}, {
+			"status": "released",
+			"uuid": "2d661756-da8c-44be-ab49-321ec5763ae0",
+			"href": "/files-processed/4DNFIY3DJCHA/@@download/4DNFIY3DJCHA.normvector.juicerformat.gz",
+			"@type": ["FileProcessed", "File", "Item"],
+			"display_title": "4DNFIY3DJCHA.normvector.juicerformat.gz",
+			"@id": "/files-processed/4DNFIY3DJCHA/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"last_modified": {
+				"date_modified": "2018-09-18T04:20:31.616215+00:00"
+			},
+			"file_format": {
+				"@id": "/file-formats/normvector_juicerformat/",
+				"uuid": "d13d06cf-218e-4f61-55f0-94f226118b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"@type": ["FileFormat", "Item"],
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+		"body": "<b>In Situ Hi-C</b>\n\n<p>\n   In situ Hi-C is a method to detect and quantify the pairwise interactions between chromosome regions across the entire genome. It was developed in 2014 as an improvement over the dilution Hi-C method. Compared to standard dilution Hi-C, this technique reduces the frequency of random ligation because the ligation is performed <i>in situ</i> inside the nucleus, a constrained space, instead of in solution, where DNA fragments are floating freely. In addition, this protocol can be done more quickly in the lab and was the first to introduce the use of 4-cutter restriction enzymes as opposed to the previous 6-cutters, providing higher resolution.\n</p>\n<p>\nThe protocol involves cross-linking the cells with formaldehyde to form links between physically adjacent DNA regions. The cells are then permeabilized with their nuclei intact. A 4-cutter restriction enzyme is used to digest the chromatin into multiple DNA fragments. The resulting fragments are biotinylated by end filling of the fragments ends. The fragments are then ligated and the DNA is purified and sheared. The biotinylated fragments are pulled down from the solution with streptavidin beads and a library is constructed and sequenced. Analysis of the resulting paired-end short read sequences produces a matrix that shows the number of interactions between different DNA regions.\n</p>\n<p>\nSee <a href=\"https://www.sciencedirect.com/science/article/pii/S0092867414014974?via%3Dihub\">Rao et al., 2014</a> for more details.\n</p>\n\n<div>\n<img style=\"width: 600px;\" src=\"https://s3.amazonaws.com/4dn-dcic-public/static-pages/InfoBoxes/IsHC_fig1.png\"/>\n  <br/><br/>\n  <em>Image source: Rao et al., 2014, Figure 1A</em>\n</div>",
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+		"content": "<b>In Situ Hi-C</b>\n\n<p>\n   In situ Hi-C is a method to detect and quantify the pairwise interactions between chromosome regions across the entire genome. It was developed in 2014 as an improvement over the dilution Hi-C method. Compared to standard dilution Hi-C, this technique reduces the frequency of random ligation because the ligation is performed <i>in situ</i> inside the nucleus, a constrained space, instead of in solution, where DNA fragments are floating freely. In addition, this protocol can be done more quickly in the lab and was the first to introduce the use of 4-cutter restriction enzymes as opposed to the previous 6-cutters, providing higher resolution.\n</p>\n<p>\nThe protocol involves cross-linking the cells with formaldehyde to form links between physically adjacent DNA regions. The cells are then permeabilized with their nuclei intact. A 4-cutter restriction enzyme is used to digest the chromatin into multiple DNA fragments. The resulting fragments are biotinylated by end filling of the fragments ends. The fragments are then ligated and the DNA is purified and sheared. The biotinylated fragments are pulled down from the solution with streptavidin beads and a library is constructed and sequenced. Analysis of the resulting paired-end short read sequences produces a matrix that shows the number of interactions between different DNA regions.\n</p>\n<p>\nSee <a href=\"https://www.sciencedirect.com/science/article/pii/S0092867414014974?via%3Dihub\">Rao et al., 2014</a> for more details.\n</p>\n\n<div>\n<img style=\"width: 600px;\" src=\"https://s3.amazonaws.com/4dn-dcic-public/static-pages/InfoBoxes/IsHC_fig1.png\"/>\n  <br/><br/>\n  <em>Image source: Rao et al., 2014, Figure 1A</em>\n</div>"
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+		"abstract": "Over the past decade, 3C-related methods, complemented by increasingly detailed microscopic views of the nucleus, have provided unprecedented insights into chromosome folding in vivo. Here, to overcome the resolution limits inherent to the majority of genome-wide chromosome architecture mapping studies, we extend a recently-developed Hi-C variant, Micro-C, to map chromosome architecture at nucleosome resolution in human embryonic stem cells and fibroblasts. Micro-C maps robustly capture well-described features of mammalian chromosome folding including A/B compartment organization, topologically associating domains (TADs), and cis interaction peaks anchored at CTCF binding sites, while also providing a detailed 1-dimensional map of nucleosome positioning and phasing genome-wide. Compared to high-resolution in situ Hi-C, Micro-C exhibits substantially improved signal-to-noise with an order of magnitude greater dynamic range, enabling not only localization of domain boundaries with single-nucleosome accuracy, but also resolving more than 20,000 additional looping interaction peaks in each cell type. Intriguingly, many of these newly-identified peaks are localized along stripe patterns and form transitive grids, consistent with their anchors being pause sites impeding the process of cohesin-dependent loop extrusion. Together, our analyses provide the highest resolution maps of chromosome folding in human cells to date, and provide a valuable resource for studies of chromosome folding mechanisms.",
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+				"title": "Unmapped Reads",
+				"tooltip": "Percent of total reads (=579.98m)"
+			}, {
+				"numberType": "percent",
+				"value": "22.216",
+				"title": "Multimapped Reads",
+				"tooltip": "Percent of total reads (=709.01m)"
+			}, {
+				"numberType": "percent",
+				"value": "21.79",
+				"title": "Duplicate Reads",
+				"tooltip": "Percent of total reads (=695.43m)"
+			}, {
+				"numberType": "percent",
+				"value": "0.007",
+				"title": "Walks",
+				"tooltip": "Percent of total reads (=0.21m)"
+			}, {
+				"numberType": "percent",
+				"value": "0.172",
+				"title": "Minor Contigs",
+				"tooltip": "Percent of total reads (=5.49m)"
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+			"quality_metric": {
+				"@id": "/quality-metrics-bamqc/c8a903b2-2fee-4c75-837e-6ef6764b7252/",
+				"uuid": "c8a903b2-2fee-4c75-837e-6ef6764b7252",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"@type": ["QualityMetricBamqc", "QualityMetric", "Item"],
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricBamqc from 2019-12-21"
+			},
+			"file_size": 272111684753,
+			"extra_files": [],
+			"file_type_detailed": "alignments (bam)"
+		}, {
+			"status": "released",
+			"uuid": "0401edff-028d-48cf-b3a9-ac58f35a4a3b",
+			"file_classification": "processed file",
+			"href": "/files-processed/4DNFILR6CKEJ/@@download/4DNFILR6CKEJ.pairs.gz",
+			"@type": ["FileProcessed", "File", "Item"],
+			"upload_key": "0401edff-028d-48cf-b3a9-ac58f35a4a3b/4DNFILR6CKEJ.pairs.gz",
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+				"display_title": "pairs"
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+				"value": "1206875267",
+				"title": "Filtered Reads"
+			}, {
+				"numberType": "percent",
+				"value": "30.012",
+				"title": "Cis reads (>20kb)",
+				"tooltip": "Percent of total reads (=362.21m)"
+			}, {
+				"numberType": "percent",
+				"value": "16.678",
+				"title": "Short cis reads",
+				"tooltip": "Percent of total reads (=201.28m)"
+			}, {
+				"numberType": "percent",
+				"value": "53.31",
+				"title": "Trans Reads",
+				"tooltip": "Percent of total reads (=643.39m)"
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+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webprod-wfoutput/4DNFILR6CKEJ/pairsqc_report.html",
+				"@id": "/quality-metrics-pairsqc/82ade5a4-8154-4b8a-8ff8-4f41acbab04a/",
+				"uuid": "82ade5a4-8154-4b8a-8ff8-4f41acbab04a",
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+				"@type": ["QualityMetricPairsqc", "QualityMetric", "Item"],
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricPairsqc from 2018-03-06"
+			},
+			"file_size": 25008910992,
+			"extra_files": [{
+				"href": "/files-processed/4DNFILR6CKEJ/@@download/4DNFILR6CKEJ.pairs.gz.px2",
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+			}],
+			"file_type_detailed": "contact list-replicate (pairs)"
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+						"edit": ["group.admin"]
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+				"display_title": "fastq",
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+			"@id": "/files-fastq/4DNFIO9OHYUV/",
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+				"edit": ["group.admin"]
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+			"file_type_detailed": "reads (fastq)",
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+			"display_title": "4DNFIO9OHYUV.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webprod-wfoutput/4DNFIO9OHYUV/fastqc_report.html",
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+				"principals_allowed": {
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+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 331456009,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-11"
+			},
+			"paired_end": "1",
+			"extra_files": [],
+			"file_size": 9897412116
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+			"href": "/files-fastq/4DNFI3TE7QNY/@@download/4DNFI3TE7QNY.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
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+			"display_title": "4DNFI3TE7QNY.fastq.gz",
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+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webdev-wfoutput/4DNFI3TE7QNY/fastqc_report.html",
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+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 359709230,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-10"
+			},
+			"paired_end": "1",
+			"extra_files": [],
+			"file_size": 10729323194
+		}, {
+			"status": "released",
+			"md5sum": "cbd8d018e2947ee74604da5319775fb6",
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+			"href": "/files-fastq/4DNFIN9HJDNO/@@download/4DNFIN9HJDNO.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "147fc064-39d6-4689-bf43-9c266e169d0a/4DNFIN9HJDNO.fastq.gz",
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+					"uuid": "83cdd1d0-779d-46f9-970e-56bc4e814d3e",
+					"accession": "4DNFIBZJ8W8V",
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+					"paired_end": "2",
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+						"edit": ["group.admin"]
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+				"display_title": "fastq",
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+			},
+			"@id": "/files-fastq/4DNFIN9HJDNO/",
+			"principals_allowed": {
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+				"edit": ["group.admin"]
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+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFIN9HJDNO.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webprod-wfoutput/4DNFIN9HJDNO/fastqc_report.html",
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+					"edit": ["group.admin"]
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+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 357393834,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-11"
+			},
+			"paired_end": "1",
+			"extra_files": [],
+			"file_size": 10813983102
+		}, {
+			"status": "released",
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+			"accession": "4DNFI6LI5AMP",
+			"href": "/files-fastq/4DNFI6LI5AMP/@@download/4DNFI6LI5AMP.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "4062cab9-c32a-4355-81bf-c42e45b971b3/4DNFI6LI5AMP.fastq.gz",
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+					"uuid": "4d3860a8-7f01-49e8-b474-70b6ac1b92a8",
+					"accession": "4DNFIEIHJLLV",
+					"@type": ["FileFastq", "File", "Item"],
+					"paired_end": "2",
+					"file_type": "reads",
+					"principals_allowed": {
+						"view": ["system.Everyone"],
+						"edit": ["group.admin"]
+					},
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+				"relationship_type": "paired with"
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+			"@id": "/files-fastq/4DNFI6LI5AMP/",
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+				"edit": ["group.admin"]
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+			"display_title": "4DNFI6LI5AMP.fastq.gz",
+			"quality_metric": {
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+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 354594591,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-11"
+			},
+			"paired_end": "1",
+			"extra_files": [],
+			"file_size": 10974982284
+		}, {
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+			"md5sum": "da0f9415d661765b90cb666937adc18f",
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+			"accession": "4DNFIU8LC2OB",
+			"href": "/files-fastq/4DNFIU8LC2OB/@@download/4DNFIU8LC2OB.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "5d0b3e21-6f3c-41d0-9807-6bd6ef713eec/4DNFIU8LC2OB.fastq.gz",
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+					"uuid": "b8136e93-6b93-4ce3-a11f-64f949591fe6",
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+			"@id": "/files-fastq/4DNFIQQ1T6EJ/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFIQQ1T6EJ.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webdev-wfoutput/4DNFIQQ1T6EJ/fastqc_report.html",
+				"@id": "/quality-metrics-fastqc/42e6458b-6a21-47be-901e-1fa5b50c687b/",
+				"uuid": "42e6458b-6a21-47be-901e-1fa5b50c687b",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 363446443,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-10"
+			},
+			"paired_end": "2",
+			"extra_files": [],
+			"file_size": 12113567365
+		}, {
+			"status": "released",
+			"md5sum": "2e5aaa9ca44f0956f48652a6d69d3ffa",
+			"uuid": "c1f21f6c-26b5-47ce-81aa-4c38b1082a56",
+			"accession": "4DNFIF3HEOMM",
+			"href": "/files-fastq/4DNFIF3HEOMM/@@download/4DNFIF3HEOMM.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "c1f21f6c-26b5-47ce-81aa-4c38b1082a56/4DNFIF3HEOMM.fastq.gz",
+			"related_files": [{
+				"file": {
+					"@id": "/files-fastq/4DNFICIEIA1P/",
+					"uuid": "5d6b01b5-e2aa-4a4f-8f4b-eed599941bc0",
+					"accession": "4DNFICIEIA1P",
+					"@type": ["FileFastq", "File", "Item"],
+					"paired_end": "2",
+					"file_type": "reads",
+					"principals_allowed": {
+						"view": ["system.Everyone"],
+						"edit": ["group.admin"]
+					},
+					"display_title": "4DNFICIEIA1P.fastq.gz"
+				},
+				"relationship_type": "paired with"
+			}],
+			"file_type": "reads",
+			"file_format": {
+				"@id": "/file-formats/fastq/",
+				"uuid": "c13d06cf-218e-4f61-aaf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"display_title": "fastq",
+				"@type": ["FileFormat", "Item"]
+			},
+			"@id": "/files-fastq/4DNFIF3HEOMM/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFIF3HEOMM.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webdev-wfoutput/4DNFIF3HEOMM/fastqc_report.html",
+				"@id": "/quality-metrics-fastqc/97cb89e2-3c08-4c7e-95b2-89a1af372d4a/",
+				"uuid": "97cb89e2-3c08-4c7e-95b2-89a1af372d4a",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 359872510,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-10"
+			},
+			"paired_end": "1",
+			"extra_files": [],
+			"file_size": 10687836913
+		}, {
+			"status": "released",
+			"md5sum": "8f26bb3e2895588ff65e3a335a304c48",
+			"uuid": "5d6b01b5-e2aa-4a4f-8f4b-eed599941bc0",
+			"accession": "4DNFICIEIA1P",
+			"href": "/files-fastq/4DNFICIEIA1P/@@download/4DNFICIEIA1P.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "5d6b01b5-e2aa-4a4f-8f4b-eed599941bc0/4DNFICIEIA1P.fastq.gz",
+			"related_files": [{
+				"file": {
+					"@id": "/files-fastq/4DNFIF3HEOMM/",
+					"uuid": "c1f21f6c-26b5-47ce-81aa-4c38b1082a56",
+					"accession": "4DNFIF3HEOMM",
+					"@type": ["FileFastq", "File", "Item"],
+					"paired_end": "1",
+					"file_type": "reads",
+					"principals_allowed": {
+						"view": ["system.Everyone"],
+						"edit": ["group.admin"]
+					},
+					"display_title": "4DNFIF3HEOMM.fastq.gz"
+				},
+				"relationship_type": "paired with"
+			}],
+			"file_type": "reads",
+			"file_format": {
+				"@id": "/file-formats/fastq/",
+				"uuid": "c13d06cf-218e-4f61-aaf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"display_title": "fastq",
+				"@type": ["FileFormat", "Item"]
+			},
+			"@id": "/files-fastq/4DNFICIEIA1P/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFICIEIA1P.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webprod-wfoutput/4DNFICIEIA1P/fastqc_report.html",
+				"@id": "/quality-metrics-fastqc/679f47d8-6ae3-4e64-95a7-991cb56172b2/",
+				"uuid": "679f47d8-6ae3-4e64-95a7-991cb56172b2",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 359872510,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-11"
+			},
+			"paired_end": "2",
+			"extra_files": [],
+			"file_size": 11851176492
+		}, {
+			"status": "released",
+			"md5sum": "700340a76ffbae013d7ef8ac6d8acdad",
+			"uuid": "7bfd8442-ebfa-4dda-9e34-35772a005325",
+			"accession": "4DNFI6LY81F9",
+			"href": "/files-fastq/4DNFI6LY81F9/@@download/4DNFI6LY81F9.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "7bfd8442-ebfa-4dda-9e34-35772a005325/4DNFI6LY81F9.fastq.gz",
+			"related_files": [{
+				"file": {
+					"@id": "/files-fastq/4DNFIYC5ZZGF/",
+					"uuid": "bbb1dc38-3cd6-49bd-ae8a-bd355f9b7e06",
+					"accession": "4DNFIYC5ZZGF",
+					"@type": ["FileFastq", "File", "Item"],
+					"paired_end": "1",
+					"file_type": "reads",
+					"principals_allowed": {
+						"view": ["system.Everyone"],
+						"edit": ["group.admin"]
+					},
+					"display_title": "4DNFIYC5ZZGF.fastq.gz"
+				},
+				"relationship_type": "paired with"
+			}],
+			"file_type": "reads",
+			"file_format": {
+				"@id": "/file-formats/fastq/",
+				"uuid": "c13d06cf-218e-4f61-aaf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"display_title": "fastq",
+				"@type": ["FileFormat", "Item"]
+			},
+			"@id": "/files-fastq/4DNFI6LY81F9/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFI6LY81F9.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webprod-wfoutput/4DNFI6LY81F9/fastqc_report.html",
+				"@id": "/quality-metrics-fastqc/340406ad-d7a4-436a-8d56-05872f770b0b/",
+				"uuid": "340406ad-d7a4-436a-8d56-05872f770b0b",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 359340353,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-11"
+			},
+			"paired_end": "2",
+			"extra_files": [],
+			"file_size": 12586105662
+		}, {
+			"status": "released",
+			"md5sum": "3406597308b13a0b9e8f42d90bde9744",
+			"uuid": "8ff3cb1b-30c5-462e-a234-246f30747c1d",
+			"accession": "4DNFIM62WI62",
+			"href": "/files-fastq/4DNFIM62WI62/@@download/4DNFIM62WI62.fastq.gz",
+			"@type": ["FileFastq", "File", "Item"],
+			"upload_key": "8ff3cb1b-30c5-462e-a234-246f30747c1d/4DNFIM62WI62.fastq.gz",
+			"related_files": [{
+				"file": {
+					"@id": "/files-fastq/4DNFIO9OHYUV/",
+					"uuid": "cb2dbe53-2f7a-4542-9843-2d8265805733",
+					"accession": "4DNFIO9OHYUV",
+					"@type": ["FileFastq", "File", "Item"],
+					"paired_end": "1",
+					"file_type": "reads",
+					"principals_allowed": {
+						"view": ["system.Everyone"],
+						"edit": ["group.admin"]
+					},
+					"display_title": "4DNFIO9OHYUV.fastq.gz"
+				},
+				"relationship_type": "paired with"
+			}],
+			"file_type": "reads",
+			"file_format": {
+				"@id": "/file-formats/fastq/",
+				"uuid": "c13d06cf-218e-4f61-aaf0-91f226248b2c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"display_title": "fastq",
+				"@type": ["FileFormat", "Item"]
+			},
+			"@id": "/files-fastq/4DNFIM62WI62/",
+			"principals_allowed": {
+				"view": ["system.Everyone"],
+				"edit": ["group.admin"]
+			},
+			"file_type_detailed": "reads (fastq)",
+			"file_classification": "raw file",
+			"display_title": "4DNFIM62WI62.fastq.gz",
+			"quality_metric": {
+				"url": "https://s3.amazonaws.com/elasticbeanstalk-fourfront-webdev-wfoutput/4DNFIM62WI62/fastqc_report.html",
+				"@id": "/quality-metrics-fastqc/b0e44d9f-c1eb-40d5-bac5-539c9ad7c84c/",
+				"uuid": "b0e44d9f-c1eb-40d5-bac5-539c9ad7c84c",
+				"principals_allowed": {
+					"view": ["system.Everyone"],
+					"edit": ["group.admin"]
+				},
+				"Sequence length": "50",
+				"@type": ["QualityMetricFastqc", "QualityMetric", "Item"],
+				"Total Sequences": 331456009,
+				"overall_quality_status": "PASS",
+				"display_title": "QualityMetricFastqc from 2017-08-10"
+			},
+			"paired_end": "2",
+			"extra_files": [],
+			"file_size": 10690007361
+		}],
+		"principals_allowed": {
+			"view": ["system.Everyone"],
+			"edit": ["group.admin"]
+		}
+	}]
+}
diff --git a/test/test_ff_utils.py b/test/test_ff_utils.py
index 4eb88d0b8..29d0e881c 100644
--- a/test/test_ff_utils.py
+++ b/test/test_ff_utils.py
@@ -2,6 +2,7 @@
 import json
 import time
 from dcicutils import ff_utils
+from unittest import mock
 pytestmark = pytest.mark.working
 
 
@@ -79,6 +80,18 @@ def profiles():
     }
 
 
+@pytest.fixture
+def mocked_replicate_experiment():
+    with open('./test/data_files/test_experiment_set.json') as opf:
+        return json.load(opf)
+
+
+@pytest.fixture
+def qc_metrics():
+    with open('./test/data_files/qc_metrics.json') as opf:
+        return json.load(opf)
+
+
 def test_generate_rand_accession():
     test = ff_utils.generate_rand_accession()
     assert '4DN' in test
@@ -804,7 +817,7 @@ def test_faceted_search_users(integrated_ff):
                        'ff_env': ff_env,
                        'item_facets': all_facets}
     resp = ff_utils.faceted_search(**neg_affiliation)
-    assert len(resp) == 23
+    assert len(resp) == 24
     neg_affiliation = {'item_type': 'user',
                        'Affiliation': '-4DN Testing Lab',
                        'key': key,
@@ -815,28 +828,65 @@ def test_faceted_search_users(integrated_ff):
     assert len(resp) == 10
 
 
+def test_fetch_qc_metrics_logic(mocked_replicate_experiment):
+    """
+    Tests that the fetch_qc_metrics function is being used correctly inside the get_associated_qc_metrics function
+    """
+    with mock.patch("dcicutils.ff_utils.get_metadata") as mock_get_metadata:
+        mock_get_metadata.return_value = {
+            "uuid": "7a2d8f3d-2108-4b81-a09e-fdcf622a0392",
+            "display_title": "QualityMetricPairsqc from 2018-04-27"
+        }
+        result = ff_utils.fetch_files_qc_metrics(mocked_replicate_experiment, ['processed_files'])
+        assert "7a2d8f3d-2108-4b81-a09e-fdcf622a0392" in result
+
+
+def test_get_qc_metrics_logic(mocked_replicate_experiment, qc_metrics):
+    """
+    End to end test on 'get_associated_qc_metrics' to check the logic of the fuction to make sure
+    it is getting the qc metrics.
+    """
+    with mock.patch("dcicutils.ff_utils.get_metadata") as mock_get_metadata:
+        mock_get_metadata.return_value = mocked_replicate_experiment
+        with mock.patch("dcicutils.ff_utils.fetch_files_qc_metrics") as mock_fetch_qc:
+            mock_fetch_qc.return_value = qc_metrics
+            result = ff_utils.get_associated_qc_metrics("6ba6a5df-dac5-4111-b5f0-299b6bee0f38")
+            assert "762d3cc0-fcd4-4c1a-a99f-124b0f371690" in result
+            assert "9b0b1733-0f17-420e-9e38-1f58ba993c54" in result
+
+
 @pytest.mark.integrated
 def test_get_qc_metrics(integrated_ff):
     """
     Tests that we correctly extract qc metric uuids (and therefore items) from the helper
     """
+
     key, ff_env = integrated_ff['ff_key'], integrated_ff['ff_env']
     uuid = '331106bc-8535-3338-903e-854af460b544'
     qc_metrics = ff_utils.get_associated_qc_metrics(uuid, key=key, ff_env=ff_env)
     assert len(qc_metrics.keys()) == 1
     assert '131106bc-8535-4448-903e-854abbbbbbbb' in qc_metrics
-    assert 'QualityMetric' in qc_metrics['131106bc-8535-4448-903e-854abbbbbbbb']['@type']
+    target_qc = qc_metrics['131106bc-8535-4448-903e-854abbbbbbbb']
+    assert 'QualityMetric' in target_qc['values']['@type']
+    assert target_qc['organism'] == 'human'
+    assert target_qc['experiment_type'] == 'Dilution Hi-C'
+    assert target_qc['experiment_subclass'] == 'Hi-C'
+    assert target_qc['source_file_association'] == 'processed_files'
+    assert target_qc['source_experiment'] == '4DNEXO67APV1'
+    assert target_qc['source_experimentSet'] == '4DNESOPFAAA1'
+    assert target_qc['biosource_summary'] == "GM12878"
+
     kwargs = {  # do same as above w/ kwargs, specify to include raw files this time
         'key': key,
         'ff_env': ff_env,
-        'exclude_raw_files': False
+        'include_raw_files': True
     }
     qc_metrics = ff_utils.get_associated_qc_metrics(uuid, **kwargs)
     assert len(qc_metrics.keys()) == 2
-    assert '4c9dabc6-61d6-4054-a951-c4fdd0023800' in qc_metrics
     assert '131106bc-8535-4448-903e-854abbbbbbbb' in qc_metrics
-    assert 'QualityMetric' in qc_metrics['131106bc-8535-4448-903e-854abbbbbbbb']['@type']
-    assert 'QualityMetric' in qc_metrics['4c9dabc6-61d6-4054-a951-c4fdd0023800']['@type']
+    assert '4c9dabc6-61d6-4054-a951-c4fdd0023800' in qc_metrics
+    assert 'QualityMetric' in qc_metrics['131106bc-8535-4448-903e-854abbbbbbbb']['values']['@type']
+    assert 'QualityMetric' in qc_metrics['4c9dabc6-61d6-4054-a951-c4fdd0023800']['values']['@type']
 
 
 @pytest.mark.integrated
@@ -936,7 +986,7 @@ def test_search_es_metadata(integrated_ff):
     """ Tests search_es_metadata on mastertest """
     res = ff_utils.search_es_metadata('fourfront-mastertestuser', {'size': '1000'},
                                       key=integrated_ff['ff_key'], ff_env=integrated_ff['ff_env'])
-    assert len(res) == 27
+    assert len(res) == 28
     test_query = {
         'query': {
             'bool': {